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Posts posted by Doc Reeves
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9 minutes ago, TâBoo Ted Marshall said:
If Stoops and Mixon can overcome video proof of a female beatdown, Urbs will skate. It's just they way these things go. Â
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Absolutely this.Â
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After multipule OU fiascos and the Baylor horror show I doubt the dickless NCAA will do shit. The way they treat this shit is practically the text book definition of misogyny
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10 wins.
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fuck it, I like miseryÂ
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Makes sense. tOSU undergrads are like a mid-west, less coherent version of Jersey Shore.Â
Grad schools are pretty darn good thoÂ
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Tang seems like heâs a great guy, but thereâs no chance this guy fucks.
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A teenager from Texas has taken quantum computing down a notch. In a paper posted online earlier this month, 18-year-old Ewin Tang proved that ordinary computers can solve an important computing problem with performance potentially comparable to that of a quantum computer.
In its most practical form, the ârecommendation problemâ relates to how services like Amazon and Netflix determine which products you might like to try. Computer scientists had considered it to be one of the best examples of a problem thatâs exponentially faster to solve on quantum computers â making it an important validation of the power of these futuristic machines. Now Tang has stripped that validation away.
âThis was one of the most definitive examples of a quantum speedup, and itâs no longer there,â said Tang, who graduated from the University of Texas, Austin, in spring and will begin a Ph.D. at the University of Washington in the fall.
In 2014, at age 14 and after skipping the fourth through sixth grades, Tang enrolled at UT Austin and majored in mathematics and computer science. In the spring of 2017 Tang took a class on quantum information taught by Scott Aaronson, a prominent researcher in quantum computing. Aaronson recognized Tang as an unusually talented student and offered himself as adviser on an independent research project. Aaronson gave Tang a handful of problems to choose from, including the recommendation problem. Tang chose it somewhat reluctantly.
âI was hesitant because it seemed like a hard problem when I looked at it, but it was the easiest of the problems he gave me,â Tang said.
The recommendation problem is designed to give a recommendation for products that users will like. Consider the case of Netflix. It knows what films youâve watched. It knows what all of its other millions of users have watched. Given this information, what are you likely to want to watch next?
You can think of this data as being arranged in a giant grid, or matrix, with movies listed across the top, users listed down the side, and values at points in the grid quantifying whether, or to what extent, each user likes each film. A good algorithm would generate recommendations by quickly and accurately recognizing similarities between movies and users and filling in the blanks in the matrix.
In 2016 the computer scientists Iordanis Kerenidis and Anupam Prakash published a quantum algorithm that solved the recommendation problem exponentially faster than any known classical algorithm. They achieved this quantum speedup in part by simplifying the problem: Instead of filling out the entire matrix and identifying the single best product to recommend, they developed a way of sorting users into a small number of categories â do they like blockbusters or indie films? â and sampling the existing data in order to generate a recommendation that was simply good enough.
At the time of Kerenidis and Prakashâs work, there were only a few examples of problems that quantum computers seemed to be able to solve exponentially faster than classical computers. Most of those examples were specialized â they were narrow problems designed to play to the strengths of quantum computers (these include the âforrelationâ problem Quanta covered earlier this year). Kerenidis and Prakashâs result was exciting because it provided a real-world problem people cared about where quantum computers outperformed classical ones.
âTo my sense it was one of the first examples in machine learning and big data where we showed quantum computers can do something that we still donât know how to do classically,â said Kerenidis, a computer scientist at the Research Institute on the Foundations of Computer Science in Paris.
Kerenidis and Prakash proved that a quantum computer could solve the recommendation problem exponentially faster than any known algorithm, but they didnât prove that a fast classical algorithm couldnât exist. So when Aaronson began working with Tang in 2017, that was the question he posed â prove there is no fast classical recommendation algorithm, and thereby confirm Kerenidis and Prakashâs quantum speedup is real.
âThat seemed to me like an important âtâ to cross to complete this story,â said Aaronson, who believed at the time that no fast classical algorithm existed.
Tang set to work in the fall of 2017, intending for the recommendation problem to serve as a senior thesis. For several months Tang struggled to prove that a fast classical algorithm was impossible. As time went on, Tang started to think that maybe such an algorithm was possible after all.
âI started believing there is a fast classical algorithm, but I couldnât really prove it to myself because Scott seemed to think there wasnât one, and he was the authority,â Tang said.
Finally, with the senior thesis deadline bearing down, Tang wrote to Aaronson and admitted a growing suspicion: âTang wrote to me saying, actually, âI think there is a fast classical algorithm,ââ Aaronson said.
Throughout the spring Tang wrote up the results and worked with Aaronson to clarify some steps in the proof. The fast classical algorithm Tang found was directly inspired by the fast quantum algorithm Kerenidis and Prakash had found two years earlier. Tang showed that the kind of quantum sampling techniques they used in their algorithm could be replicated in a classical setting. Like Kerenidis and Prakashâs algorithm, Tangâs algorithm ran in polylogarithmic time â meaning the computational time scaled with the logarithm of characteristics like the number of users and products in the data set â and was exponentially faster than any previously known classical algorithm.
Once Tang had completed the algorithm, Aaronson wanted to be sure it was correct before releasing it publicly. âI was still nervous that once Tang put the paper online, if itâs wrong, the first big paper of [Tangâs] career would go splat,â Aaronson said.
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Ive been working all day and just got the news.Â
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Just wanted to stop by and offer Urban a stern shaking of the head and hardy âtisk, tisk, tiskâ
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Of course the offseason gets good right at the end.Â
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Awesome. That shit slows down my drink serviceÂ
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We just gonna rename errythangÂ
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I liked it. Good movie. Non stop action and incredible shots of stunts. A few WTF? plot hole moments but overall a very good action film.Â
Tom may be batshit crazy, but heâs consistently good in everything he does on screen
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Heâll do
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1996- Atlanta Olympic Games BombingÂ
1981- Adam Walsh is kidnapped from a mall.Â
1866- first permanent trans-Atlantic telegraph wire laid.
1890- Vincent Van Gogh shoots himself. He dies 2 days later
1929- Geneva Convention is signed by 53 nations.
1940-  A Wild Hare is released introducing the world to bugs bunny.
1953- Fighting in the Korean War ceases
2002- Ukraine Airshow Disaster, worst ever with 85 dead
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its also Yahoo Serious and Alex Rodriguezâs Birthday today
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Just now, DaysOff said:
Having served I have zero faith in the jury of your peers shit. Tossup the skates because morons.
A really, really good attorney told me once that if you are ever on a murder jury, it means both sides think you are a Forrest Gump caliber moron.
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Here we go
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Joined late 90s and would cruse it at the public terminals at the PCL. It was sometimes The only thing that would keep me from skipping my 2nd class on m/w/f.
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got banned when I posted that I didnât think GG looked like a good College QB on the corresponding circle jerk commit celebration page.  Never went back. Started lurking on Shaggy in early â08.
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So sad
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Newsflash to people who donât sail:Â Duckboats are death traps.Â
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8 minutes ago, Hank Scorpio said:
Anyone who thinks Herman is a better coach than Strong is a racist.Â
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Thereâs a cloak room style Trump joke in there somewhereÂ
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JfC whogivesashitÂ
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Weâre playing FuckMarryKill?
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All these NOVA/Smithsonian specials and anticipation are bullshit
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Lets get this done Earth!
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5 minutes ago, TreatyOak said:
"Iâd really appreciate it if anyone on the current staff at the University of Texas Football team besides Craig Naivar , Jason Washington or Kyle Coats would keep my name out of there mouth and continue to bad talk me or any other junior that decided to leave early."
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If Deshon Elliott had used Grammarly:
I would really appreciate it if the staff of the University of Texas football team, aside from Craig Naivar, Jason Washington or Kyle Coats, would not speak ill about me moving forward. Additionally, I would like this request to extend to the other UT student athletes who left before their senior year for the NFL. Thank you for your consideration.   Â
Daymn. Thatâs the full version.Â
We truly are the Stanford of the Souf...
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Thanks Justin.  Iâll take this drop of rain in the desert.
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:looooong exhale:
It's time to monetize, the question is how?
in Please do this better?
Posted
Let me know what you all decide. Iâm down