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Antitrust crusaders have built up serious momentum in Washington, but so far, it’s all been theory and talk. Groups like Open Markets have made a strong case that big companies (especially big tech companies) are distorting the market to drive out competitors. We need a new standard for monopolies, they argue, one that focuses less on consumer harm and more on the skewed incentives produced by a company the size of Facebook or Google.

Someday soon, those ideas will be put to the test, probably against one of a handful of companies. For anti-monopolists, it’s a chance to reshape tech into something more democratic and less destructive. It’s just a question of which company makes the best target.

To that end, here’s the case against four of the movement’s biggest targets, and what they might look like if they came out on the losing end. (Note: Apple was too much of a conventional retailer to make the list, but if you’re wondering what an antitrust lawsuit against Cupertino might look like, this is a pretty good place to start.)

Continue reading “How to break up Facebook, Google, and other tech giants” »

http://www.blogtalkradio.com/unlimited-realities-with-lisa-z…lth-future

A psychologist’s first reaction to his own study was:

Here’s a slider photo of Planet satellite images showing Barangay Ucab, Itogon, Benguet before and after the Typhoon #OmpongPH-induced landslide event.

Create stories with Planet.

Researchers from the University of Tokyo have created the strongest-ever controllable magnetic field, and it could help make nuclear fusion a reality.

Computer scientists at MIT have developed a machine-learning system that can identify objects in an image based on a spoken description of the image.

Typical speech recognition systems like Google Voice and Siri rely on transcriptions of thousands of hours of speech recordings, which are then used to map speech signals to specific words.

Still in its early stages, the MIT system learns words from recorded speech clips and objects in images and then links them. Several hundred different works and objects can be recognized so far, with expectations that future versions can advance to a larger scale.

Continue reading “MIT Machine-Learning System IDs Objects In Photos” »

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