#Biometric Tattoos, From #Wearables To Digital Health https://wtvox.com/cyborgs-and-implantables/biometric-tattoos/ Devices connected by conductive paint, acting as body sensors
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“But now, the “digital tattoo” is another expertise they have. These “biometric tattoos” are in fact conductive paint and components. Together, they assemble into simple devices able to collect data from your body.”
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Are Polls Ruining Democracy?
http://www.newyorker.com/magazine/2015/11/16/politics-and-the-new-machine “#Datascience is the child of a rocky marriage between the academy & Silicon Valley”
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“If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan,
lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but
significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism.”
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#Lego’s Success Leads to Competitors & Spinoffs [including secondary market rental companies such as Pley]
http://www.nytimes.com/2015/11/21/business/legos-success-leads-to-competitors-and-spinoffs.html
The great chain of being sure about things
http://www.economist.com/news/briefing/21677228-technology-behind-bitcoin-lets-people-who-do-not-know-or-trust-each-other-build-dependable Overview of re-purposing the #Bitcoin blockchain for distributed records
No more of the same [security theatre]
http://www.economist.com/news/international/21678236-lot-what-passes-security-airports-more-theatrical-real-no-more Relative merits for the #TSA mining & profiling travelers v hi-tech scanning
Data mining & profiling v “predictable” scanning
Contrast US v IL
Cracking the vault http://www.economist.com/news/finance-and-economics/21676826-grip-banks-have-over-their-customers-weakening-cracking-vault Bank transactions are better data than search history or activity tracks. Who will control them?
Mentions mint & other services
Global #Shipping & the Raising of the Bayonne Bridge http://www.americanscientist.org/issues/pub/2015/4/global-shipping-and-the-raising-of-the-bayonne-bridge The ever increasing size of container ships overwhelms ports
$AAPL’s #DeepLearning Curve
http://www.bloomberg.com/news/articles/2015-10-29/apple-s-secrecy-hurts-its-ai-software-development A very secretive culture controlling Siri & your phone
Computer Vision
&…Hallucinationshttp://www.americanscientist.org/issues/id.16420,y.2015,no.5,content.true,page.1,css.print/issue.aspx Instead of training a neural network, train an image to fit it. Dreams emerge
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“The algorithm behind the deep dream images was devised by Alexander Mordvintsev, a Google software engineer in Zurich. In the blog posts he was joined by two coauthors: Mike Tyka, a biochemist, artist, and Google software engineer in Seattle; and Christopher Olah of Toronto, a software engineering intern at Google.
Here’s a recipe for deep dreaming. Start by choosing a source image and a target layer within the neural network. Present the image to the network’s input layer, and allow the recognition process to proceed normally until it reaches the target layer. Then, starting at the target layer, apply the backpropagation algorithm that corrects errors during the training process. However, instead of adjusting connection weights to improve the accuracy of the network’s response, adjust the source image to increase the amplitude of the response in the target layer. This forward-backward cycle is then repeated a number of times, and at intervals the image is resampled to increase the number of pixels.”
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Most popular coding languages, according to @GitHub are javascript (#1), Java, Ruby, PHP, Python, CSS, C++, C# & C
https://agenda.weforum.org/2015/08/the-9-most-popular-coding-languages
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“That means GitHub is a great place to gauge which of the world’s many thousands of programming languages are the most popular — especially since a popular programming language is always a good job skill for anybody to have in this age of technological transformation. Without further ado, here are the top programming languages on GitHub. No. 9 — C: The original C, invented in 1972, is still incredibly popular. That’s not least because it works on just about any computing platform ever made, and it’s super stable and understood by
programmers everywhere.
…
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linkstream2 microblog 