Should I continue picking things up where and when I can? Would it be wiser for me to go deeper into debt and get a second undergrad degree? Or should I try to go into grad school after doing some of my own studying up? Would the military be a better choice? Would it behoove me to just start trying to find STEM jobs and learn on the go (I know many times experience speaks louder to employers than a college degree might)? Or perhaps I should find a non-STEM job with a company that would allow me to transfer into that company's STEM work? I'd be particularly interested in hearing from people who have been in my position and from employers who have experience with employees who were in my position, but any insight would be welcome.
Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.
"They actually wrote down on paper, with exact proofs, something that people only dreamed existed," said Ilya Nemenman, a biophysicist at Emory University.
The results were startling. After re-running the election 100 times with a randomly drawn nonpartisan map each time, the average simulated election result was 7 or 8 U.S. House seats for the Democrats and 5 or 6 for Republicans. The maximum number of Republican seats that emerged from any of the simulations was eight. The actual outcome of the election — four Democratic representatives and nine Republicans – did not occur in any of the simulations. "If we really want our elections to reflect the will of the people, then I think we have to put in safeguards to protect our democracy so redistrictings don't end up so biased that they essentially fix the elections before they get started," says Mattingly. But North Carolina State Senator Bob Rucho is unimpressed. "I'm saying these maps aren't gerrymandered," says Rucho. "It was a matter of what the candidates actually was able to tell the voters and if the voters agreed with them. Why would you call that uncompetitive?"
Riddell's algorithm begins with the Wikipedia entries of all authors in the English language edition (PDF)—more than a million of them. His algorithm extracts information such as the article length, article age, estimated views per day, time elapsed since last revision, and so on. This produces a "public domain ranking" of all the authors that appear on Wikipedia. For example, the author Virginia Woolf has a ranking of 1,081 out of 1,011,304 while the Italian painter Giuseppe Amisani, who died in the same year as Woolf, has a ranking of 580,363. So Riddell's new ranking clearly suggests that organizations like Project Gutenberg should focus more on digitizing Woolf's work than Amisani's. Of the individuals who died in 1965 and whose work will enter the public domain next January in many parts of the world, the new algorithm picks out TS Eliot as the most highly ranked individual. Others highly ranked include Somerset Maugham, Winston Churchill, and Malcolm X.