A man was pulled to one side, grilled, and fined by cops after he hid his face from a facial-recognition system being tested on the streets of south east England.
London's Metropolitan Police was at the time running public tests of AI-powered equipment that takes photos of people out and about in the capital, and runs the pics through an image database of Brits on a watch list looking for a match.
Technology companies are running a campaign to bend research and regulation for their benefit; society must fight back, says Yochai Benkler.
The economic system is incompatible with the survival of life on Earth. It is time to design a new one
And the second item on the patent application flowchart (Fig 4) 'Determine emotional status of the users'
The PAX report identifies a total of $116 billion in current contracts between governments and the private sector to design, build, and maintain the world’s nuclear arsenals. The actual amount may be significantly higher, since all nine nuclear powers maintain some degree of opacity about their nuclear programs. “We know what we can trace,” says Susi Snyder, the report’s principal author, “but there’s definitely more out there.”
Apps als Uber zeggen dat hun dienst de verkeersdrukte verlicht. Maar in Amerikaanse steden lijkt het juist drukker te worden.
The social web translator
Fetches and converts data between social networks, HTML and JSON with microformats2, ActivityStreams 1 and 2, Atom, RSS, JSON Feed, and more.
Duplicity implements a traditional backup scheme, where the initial archive contains all information (full backup) and in the future only the changed information is added. However, here are some advantages it may have over other similar solutions:
A command-line installer for Windows
These applications of ML did not reach their full potential for three primary reasons: scarcity of sufficient data for training and testing, the inability to handle available data efficiently, and computers with insufficient computational speed to process large data sets. Earthquake catalogs, for example, were analyzed, rather than continuous waveform data, due to limitations in data storage capability as well as limitations in instrument density. For subsurface flow and transport predictions, the paucity of data was due to expensive instrumentation and measurement procedures, insufficiently resolved models etc. As we approach the exascale computational era, modeling capabilities have advanced significantly, allowing us to explore parameter and model spaces more thoroughly. We are now at the confluence of the ability to handle massive data streams ‘big data’, ultra fast and massive computers, significant increases in instrumentation density and quality, and advances in ML. As a result we are just now seeing the beginning of the new era in ML applied to geoscience problems that is marked by analyzing continuous geoscience data streams.
This Center for Nonlinear Studies conference will focus on modern applications of ML to solid earth geoscience problems, including earthquakes (both tectonic and induced), faulting, Earth imaging with a multitude of data types including seismic, gravity, electrical methods, geodesy, state of stress, etc. We will also explore topics related to geological characterization, subsurface flow and transport, etc. We limit the conference to the solid earth and exclude atmospheric, space and ocean science. This meeting will bring together leading experts with the goal of identifying synergies between these emerging research areas and identifying common themes and open questions.
The geosciences are data rich, with petabytes of readily and publicly available data. This availability, combined with the complexity of unsolved problems in the field, has motivated vigorous interest in the application of machine learning (ML) techniques. ML offers a new “lens” for viewing data and scientific hypotheses that differs from the perspective of traditional domain expertise. Initial uses of ML have tended to be limited in scope and isolated in application, but recent efforts to promote benchmark geoscientific data sets and competitions promise to propel broader, deeper, and increasingly coordinated and collaborative efforts.