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Erik Brynjolfsson is less pessimistic. An MIT economist who co-authored The Second Machine Age , he thinks automation won’t necessarily be so bad. The Luddites thought machines destroyed jobs, but they were only half right: They can also, eventually, create new ones. “A lot of skilled artisans did lose their jobs,” Brynjolfsson says, but several decades later demand for labor rose as new job categories emerged, like office work. “Average wages have been increasing for the past 200 years,” he notes. “The machines were creating wealth!”

The problem is that transition is rocky. In the short run, automation can destroy jobs more rapidly than it creates them—sure, things might be fine in a few decades, but that’s cold comfort to someone in, say, their 30s. Brynjolfsson thinks politicians should be adopting policies that ease the transition—much as in the past, when public education and progressive taxation and antitrust law helped prevent the 1 percent from hogging all the profits. “There’s a long list of ways we’ve tinkered with the economy to try and ensure shared prosperity,” he notes.

Will there be another Luddite uprising? Few of the historians thought that was likely. Still, they thought one could spy glimpses of Luddite-style analysis—questioning of whether the economy is fair—in the Occupy Wall Street protests, or even in the environmental movement. Others point to online activism, where hackers protest a company by hitting it with “denial of service” attacks by flooding it with so much traffic that it gets knocked off­line.

Perhaps one day, when Uber starts rolling out its robot fleet in earnest, angry out-of-work cabdrivers will go online—and try to jam up Uber’s services in the digital world.

“As work becomes more automated, I think that’s the obvious direction,” as Uglow notes. “In the West, there’s no point in trying to shut down a factory.”

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Clive Thompson is author of , and a contributing writer to the and . Photo: Tom Igoe.

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The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

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, a leading blockchain-based solution that is building a trusted ecosystem to enable secure and private data transfers, has announced its high profile Board of Advisors. Shyft’s inaugural group of advisors bring deep industry knowledge from international business and finance, blockchain, economics, and Know Your Customer (KYC) and Anti Money Laundering (AML) compliance. The Board of Advisors will play a critical role in providing strategic guidance across multiple dimensions in Shyft’s mission to make the global economy more efficient and effectively regulated.

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Shyft International Chairman Joseph Weinberg says: “At Shyft, we are ambitiously working to redefine the broken way our world uses and thinks about data, the same data that today defines our identity. Our group of advisors understand the dire need for a new system that allows attested data to not only be shared securely and privately, but also a system that can help build reputational identity from this data that can be used globally across borders. This type of identity based on trustworthiness and credibility helps build a global economy that empowers the 3.5 billion people that are currently ‘underidentified’ and ‘unbanked,’ meaning that they are unable to participate in regulatory economies. Our caliber of advisors is second to none in the industry, which a testament to the enormity of the problem and to Shyft as the solution.”

Shyft’s advisors have diverse backgrounds, experiences and broad networks, and can individually and collectively provide unique perspective and strategic direction on the global and complex topic of identity, compliance and privacy, and how a disruptive technology like blockchain can solve a global problem. The advisors were carefully selected to include experts in finance, business, regulation, data privacy, and compliance and AML expertise. The Shyft Board of Advisors includes:

Joseph Weinberg — Shyft Chairman; Paycase CEO; Think Tank Specialist to the OECD

Meltem Demirors —Chief Strategy Officer, CoinShares; Member of Global Future Council on Blockchain, World Economic Forum

Daniel Kornitzer — EVP and Chief Business Development Officer, Paysafe Group

Anthony Di Iorio —CEO Founder, Decentral and Jaxx; Ethereum Co-founder

Brock Pierce – Chairman, Bitcoin Foundation

Charlie Schrem — Founder, Bitcoin Foundation and Crypto.IQ

Ben Gorlick — Investor; Chief of Strategy, Paycase; Founder / COO CLoudHashing; CTO CrowdMachine, Blockstream, Peer Nova

Table 3 Frequency of successful mapping of randomly selected acronyms and initialisms to MeSH (n = 415 terms)

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The forty-six commonly used tested acronyms and initialisms had substantially higher success rates in mapping to MeSH as shown in Pink May sneakers Golden Goose Clearance Finishline 6V7a0
. As in the randomly selected data set, Ovid MEDLINE and the NLM Gateway Term Finder had the highest success rates, both at 89%. There were also wide differences in the success rates of the common acronyms and initialisms.

Table 4 Frequency of successful mapping of common acronyms and initialisms to MeSH (n = 46 terms)

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The results of this research show that successful mapping of acronyms and initialisms to MeSH is generally low across all interfaces studied. The findings also show wide differences in the rates of mapping when the tested interfaces are compared. The low success rates may be due to the random selection of test data, which includes acronyms and initialisms that may be less commonly known or used.

The differences in mapping rates may be related to how each system translates the search strategy and maps it to MeSH. According to the Help Screens in both PubMed and the NLM Gateway, both the PubMed Automatic Term Mapping feature and the NLM Gateway Term Finder utilize the Unified Medical Language System (UMLS) Metathesaurus [ 28, Free Shipping Visit New Sneakers MIAMI calfskin glitter Glitter Logo silver Jimmy Choo London For Sale The Cheapest Cheap With Mastercard xMUchr2tbN
]. Given the substantial differences in the mapping success rates between these two interfaces, it is clear that they employ different algorithms for mapping. Ovid MEDLINE, which had the highest success rate, uses its own statistical analysis to map a term to the controlled vocabulary [ 30 ].

The mapping of commonly used acronyms and initialisms was far more successful across all systems than the randomly selected set. As this portion of the study did not include a randomly selected set of test data, the results should be viewed cautiously. While the results of the commonly used terms were much better than the randomly selected set, there were some surprises. Even some very highly used terms did not map successfully in all interfaces. For example, EMT should lead to the MeSH term, “Emergency Medical Technicians.” In both the PubMed MeSH database and the PubMed Automatic Mapping feature, this did not occur. The same was true for PSA (prostate-specific antigen), EBM (evidence-based medicine), STD (sexually transmitted disease), and TB (tuberculosis).

The PubMed Automatic Term Mapping feature had the lowest success rates for both randomly selected terms and for common terms. UTI (urinary tract infections), HRT (hormone replacement therapy), and PMS (premenstrual syndrome) are some examples of common acronyms that did not map successfully in the Automatic Term Mapping feature of PubMed. In most cases, the entered term was searched in all fields when it did not map to the correct MeSH phrase. In some cases, mapping was incorrect. For example, HRT was mapped to the MeSH term “Heart” instead of “Hormone Replacement Therapy.”

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