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Canada’s largest province is to trial universal income, becoming the first North American government to test the progressive policy for decades.

Some 4,000 people in Ontario will be given at least C$16,989 (£9,850) a year under the scheme, with no conditions or restrictions attached.

Participants living in three settlements in Ontario will be selected at random to participate in the radical scheme, which advocates hail as a solution to poverty and costly bureaucracy.

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Machine learning algorithms and artificial intelligence systems influence many aspects of people’s lives: news articles, movies to watch, people to spend time with, access to credit, and even the investment of capital. Algorithms have been empowered to make such decisions and take actions for the sake of efficiency and speed. Despite these gains, there are concerns about the rapid automation of jobs (even such jobs as journalism and radiology). A better understanding of attitudes toward and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility cultures tend to ascribe to them. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic decisionmaking in public policy. The report ends with a survey of approaches for combating these problems.

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Where’s my money, you’re asking? Me too! There shouldn’t be poverty in America or people that can’t reasonably afford health insurance—it’s that simple. Below I’m resharing my TechCrunch California Governor policy article on a Universal Basic Income. If you’re in California, you will be able to vote for me to try to see this become a reality: https://techcrunch.com/2017/04/10/is-monetizing-federal-land-the-way-to-pay-for-basic-income/

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For people in that area, and it may be worth while to try reaching out to them for funding for anti aging stuff.


Why is RAND opening a Bay Area office?

The San Francisco Bay Area is really at the center of technology and transformation. That’s also been a focus at RAND since our very first report, Preliminary Design of an Experimental World-Circling Spaceship, in 1946, which foretold the creation of satellites more than a decade before Sputnik.

Today, our researchers are working on important questions related to autonomous vehicles, drones, cybersecurity, education technology, virtual medicine—the same questions driving Silicon Valley startups and billion-dollar Bay Area corporations. At the same time, we’re looking at issues surrounding social inequality, drug policy, water resource management, and transportation, all of which directly relate to the Bay Area.

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The year is 2030. Former president Donald Trump’s border wall, once considered a political inevitability, was never built. Instead, its billions of dollars of funding were poured into something the world had never seen: a strip of shared territory spanning the border between the United States and Mexico. Otra Nation, as the state is called, is a high-tech ecotopia, powered by vast solar farms and connected with a hyperloop transportation system. Biometric checks identify citizens and visitors, and relaxed trade rules have turned Otra Nation into a booming economic hub. Environmental conservation policies have maximized potable water and ameliorated a new Dust Bowl to the north. This is the future envisioned by the Made Collective, a group of architects, urban planners, and others who are proposing what they call a “shared co-nation” as a new kind of state.

Many people have imagined their own alternatives to Trump’s planned border wall, from the plausible — like a bi-national irrigation initiative — to the absurd — like an “inflatoborder” made of plastic bubbles. Made’s members insist that they’re serious about Otra Nation, though, and that they’ve got the skills to make it work. That’s almost certainly not true — but it’s also beside the point. At a time when policy proposals should be taken “seriously but not literally,” and facts are up for grabs, Otra Nation turns the slippery Trump playbook around to offer a counter-fantasy. In the words of collective member Marina Muñoz, “We can really make the complete American continent great again.”

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RAND has opened an office in the San Francisco Bay Area to foster collaboration with the region’s leaders and researchers working to solve today’s complex problems—issues including technological change and innovation, social inequality, water resource management, and transportation.

“RAND’s research and analysis in technology, science, and economic policy intersect directly with the innovation emerging from the San Francisco Bay Area,” said Michael D. Rich, president and CEO of RAND. “RAND’s new office should help strengthen awareness within the Bay Area community of our long-standing commitment to using evidence and data to help policy and decisionmakers enhance well-being in the region and beyond.”

RAND brings a unique set of tools to address these policy concerns: big-data analytics, gaming, and methods to help people make difficult decisions in the face of uncertainty. Nidhi Kalra, a senior information scientist, is leading the new office and will be convening public- and private-sector stakeholders to discuss important issues. “We want to partner with the region’s technology and innovation communities, to link our research and their expertise to make better policies and improve people’s lives,” she said.

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We’ve created the world’s first Spam-detecting AI trained entirely in simulation and deployed on a physical robot.

Our vision system successfully flagging a can of Spam for removal. The vision system is trained entirely in simulation, while the movement policy for grasping and removing the Spam is hard-coded. Our detector is able to avoid other objects, including healthy ones such as fruit and vegetables, which it never saw during training.

Deep learning-driven robotic systems are bottlenecked by data collection: it’s extremely costly to obtain the hundreds of thousands of images needed to train the perception system alone. It’s cheap to generate simulated data, but simulations diverge enough from reality that people typically retrain models from scratch when moving to the physical world.

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