If you control the code, you control the world,

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"[It's a] data token that boils you down to a data object," says Pete Kazanjy, founder of TalentBin, a service that uses social media to find job recruits (now part of Monster). That's especially true for hard numbers, said Google's SVP of "people operations" Laszlo Bock in a 2013 New York Times interview. "One of the things we've seen from all our data crunching is that GPAs are worthless as a criteria for hiring, and test scores are worthless," said Bock. A growing wave of hiring tech firms are ingesting far more information about candidates--surveys, work samples, social media posts, word choice, even facial expressions. Adding artificial intelligence (AI), they promise to assess work skills as well as personality traits like empathy, grit, and prejudice to provide a richer understanding of who the applicant is and whether they will fit.

The world's first Artificially Intelligent physicist is here, and it has already replicated a Nobel Prize-winning experiment -- one that involved creating an ultracold state of matter called Bose-Einstein condensate. This network was named by EContent Magazine to its "Trendsetting Products of 2014" list.

"[It's a] data token that boils you down to a data object," says Pete Kazanjy, founder of TalentBin, a service that uses social media to find job recruits (now part of Monster). That's especially true for hard numbers, said Google's SVP of "people operations" Laszlo Bock in a 2013 New York Times interview. "One of the things we've seen from all our data crunching is that GPAs are worthless as a criteria for hiring, and test scores are worthless," said Bock. A growing wave of hiring tech firms are ingesting far more information about candidates--surveys, work samples, social media posts, word choice, even facial expressions. Adding artificial intelligence (AI), they promise to assess work skills as well as personality traits like empathy, grit, and prejudice to provide a richer understanding of who the applicant is and whether they will fit.

The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. The team cooled the gas to around 1 microkelvin, and then handed control of the three laser beams over to the artificial intelligence to cool the trapped gas down to nanokelvin. "It may be able to come up with complicated ways humans haven't thought of to get experiments colder and make measurements more precise.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Artificial intelligence developed by a group of Australian research teams has replicated a complex experiment which won the Nobel Prize for Physics in 2001. The intelligent machine learned how to run a Bose-Einstein condensation – isolating an extremely cold gas inside a beam of laser light – in under an hour, something the team "didn't expect". Results have been published in the Scientific Reports journal. The algorithm has also been uploaded to GitHub for other researchers working on "quantum experiments". "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out," said Paul Wigley, co-lead researcher of the study and professor at the School of Physics and Engineering at the Australian National University.

"I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the Australian National University (ANU) Research School of Physics and Engineering in a statement. "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out," he added. Scientists wanted to recreate an experiment that won the 2001 Nobel Prize in Physics, which involved extremely cold gas trapped in a laser beam known as a Bose-Einstein condensate. The condensates "are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero (-273.15 The experiment involved trapping 40 million atoms at the intersection between two laser beams. The team used magnetic fields to cool the atoms down to about five millionths of a degree above absolute zero. Scientists then used the AI algorithm to control the lasers during cooling, carefully tuning the power of the two lasers to allow the most energetic atoms to escape without losing hold of the coldest ones. The AI algorithm was able to do this ten times faster than a regular non-AI program. "It is cheaper than taking a physicist everywhere with you.

When his father was diagnosed with stage IV, non-operable gastric cancer in 2007, Dr. Dimitris Bertsimas knew that combination chemotherapy was the best course of treatment. He visited several of the leading cancer hospitals in the nation--Dana Farber, Massachusetts General, MD Anderson and Memorial Sloan Kettering--to see what specific therapies they would propose for his father. "They each told me very distinct therapies, almost with no drugs in common," says Bertsimas. "I didn't know how to compare them." So Bertsimas, who is a professor of operational research at MIT did a simple back-of-the-envelope calculation.