For more than 200 years, the federal government has regularly taken an immense survey of American business called the Economic Census. Though not as well-known as the decennial census, the big population count in which enumerators tally Americans house to house, it has been conducted at least every five years since 1905, with a gap only during World War II. Its basic measurements of economic activity, like jobs and revenue, are crucially important to companies, policymakers and anyone trying to track the nation’s economic health.
The next Economic Census was supposed to start in January, five years after the previous one, as usual. But earlier this year, the Census Bureau quietly changed its deadline, pushing it back at least six months. The agency told POLITICO that it has not publicly announced the delay but confirmed that aspects of the Economic Census were “re-planned,” and the results would be out six months late.
For stateless architectures to work efficiently, as the IETF has recently declared in its study of RESTful architecture for the Internet of Things, the messages shared between components must be thorough, complete, and self-contained. Anything a receiving function needs to know about the work it needs to do, must be included within the API call that contacts it.
As engineers (including those at the IETF) have come to realize, persistent data will always be a necessary resource for both server-side applications and client-side apps. And the need for the Internet of Things (IoT) applications to maintain a reliable, persistent network of connected sensors and apparati, makes a stable and self-contained network state data platform vitally necessary.
So if the first edition of your industrial IoT platform was built on Cloud Foundry, which prizes itself for its stateless architecture, how do you reconcile that original vision with, if you will, the real state of things?
On a tropical island that marks the southern tip of China, a computer program called Lengpudashi is playing one-on-one poker against a dozen people at once, and it’s absolutely crushing them. Lengpudashi, which means “cold poker master” in Mandarin, is using a new artificial-intelligence technique to outbet and outbluff its opponents in a two-player version of Texas hold ’em.
The venue for the tournament is a modern-looking technology park in Haikou, capital of the island of Hainan. Outside, modern high-rises loom over aging neighborhoods. Those gathered to play the machine include several poker champs, some well-known Chinese investors, entrepreneurs, and CEOs, and even the odd television celebrity. The games are being broadcast online, and millions are watching. The event symbolizes a growing sense of excitement and enthusiasm for artificial intelligence in China, but there’s also a problem. Lengpudashi wasn’t made in Hainan, Beijing, or Shanghai; it was built in Pittsburgh, U.S.A.
For many in China, this simply won’t do. The country is now embarking on an unprecedented effort to master artificial intelligence.
Future economic historians may look back wryly at this period when we worshipped the divine right of capital while looking down on our ancestors who believed in the divine right of kings.
Business leaders making decisions to outsource jobs to low-wage countries or to replace workers with machines, or politicians who insist that it is “the market” that makes them unable to require companies to pay a living wage, rely on the defense that they are only following the laws of economics. But the things economists study are not natural phenomena like the laws of motion uncovered by Kepler and Newton.
Right now we’re at an inflection point, where many rules are being profoundly rewritten. Much as happened during the industrial revolution, new technology is rendering obsolete whole classes of employment while making untold new wonders possible. It is making some people very rich, and others much poorer.
Thanks to a $900,000 award from the National Science Foundation, Wake Forest University researchers are examining how this hormone affects growth and development of the roots of Arabidopsis thaliana, which is a genetic model used to provide insight into other plants. Arabidopsis is used widely because it is short-lived, grows very quickly, is small in physical size, and has the best characterized genome of all plants.
U.S. Representatives Dave Trott (MI-11) and Susan Brooks (IN-05) introduced the Internet of Medical Things Resilience Partnership Act, which creates a public-private stakeholder partnership to lay out a cybersecurity framework to protect protects Americans’ sensitive healthcare information from cyber-attacks.
In celebration of Earth Science Week, we want to highlight some of the many ways drones help us understand our planet and the changes we make to it.
Airborne drone
An airborne spectroscopy system mounted on a remote-controlled multicopter.
“Organizations/institutions are a key part of the scholarly communications ecosystem. However, we lack an openly licensed, independently run organizational identifier standard to use for common affiliation and citation use cases.”
A group of interested parties (led by UC Curation Center and California Digital Library) drafted and shared a proposal at last year’s PIDapalooza. “Based on that discussion, earlier this year Crossref, DataCite and ORCID announced the formation of an Organization Identifier Working Group and UC3 has supported this effort.” Deadline for RFI responses is November 15.
OpenAI; Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch & Pieter Abbeel
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“We show that for the task of simulated robot wrestling, a meta-learning agent can learn to quickly defeat a stronger non-meta-learning agent, and also show that the meta-learning agent can adapt to physical malfunction.”
“Today SQL is resurging. All of the major cloud providers now offer popular managed relational database services: e.g., Amazon RDS, Google Cloud SQL, Azure Database for PostgreSQL (Azure launched just this year). In Amazon’s own words, its PostgreSQL- and MySQL-compatible database Aurora database product has been the “fastest growing service in the history of AWS”. SQL interfaces on top of Hadoop and Spark continue to thrive. And just last month, Kafka launched SQL support. Your humble authors themselves are developers of a new time-series database that fully embraces SQL.”
Google Research Blog; Maya Gupta, Jan Pfeifer, and Seungil You
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“We present TensorFlow Lattice, a set of prebuilt TensorFlow Estimators that are easy to use, and TensorFlow operators to build your own lattice models.”