Saturday, May 30, 2015
Tuesday, May 26, 2015
How to Win Any Popular Game According to Data Scientists
http://www.washingtonpost.com/blogs/wonkblog/wp/2015/05/08/how-to-win-any-popular-game-according-to-data-scientists/
From Risk to tic-tac-toe, popular games involve tons of strategic decisions, probability and math. So one happy consequence of being a data nerd is that you may have an advantage at something even non-data nerds understand: winning.
So how do you win (almost) every game in existence, do you ask? Here are 20 data visualizations that offer lots of insight into the most popular games in America, including chess, Connect Four, Monopoly, Pac-Man, "Wheel of Fortune" and much more.
Battleship | Chess 1 | Chess 2 | Chess 3 | A coin toss | Connect Four | Diplomacy | "Jeopardy!" | Monopoly 1 | Monopoly 2 | Monopoly 3 | Monopoly 4 | Pac-Man | "The Price is Right" | Rock-paper-scissors | Scrabble | Texas hold ’em | Tic-tac-toe | Winning in Vegas
From Risk to tic-tac-toe, popular games involve tons of strategic decisions, probability and math. So one happy consequence of being a data nerd is that you may have an advantage at something even non-data nerds understand: winning.
So how do you win (almost) every game in existence, do you ask? Here are 20 data visualizations that offer lots of insight into the most popular games in America, including chess, Connect Four, Monopoly, Pac-Man, "Wheel of Fortune" and much more.
Battleship | Chess 1 | Chess 2 | Chess 3 | A coin toss | Connect Four | Diplomacy | "Jeopardy!" | Monopoly 1 | Monopoly 2 | Monopoly 3 | Monopoly 4 | Pac-Man | "The Price is Right" | Rock-paper-scissors | Scrabble | Texas hold ’em | Tic-tac-toe | Winning in Vegas
Tuesday, May 5, 2015
Artificial-Intelligence Experts Are in High Demand
http://www.wsj.com/articles/artificial-intelligence-experts-are-in-high-demand-1430472782
When the University of Washington’s computer-science department wanted to poach artificial-intelligence expert Carlos Guestrin from Carnegie Mellon, it turned to Amazon.com Inc.
The Seattle-based tech giant ponied up $2 million to fund two professorships: one for Mr. Guestrin, and another for his wife, who also works in the field. To seal the deal, Amazon Chief Executive Jeff Bezos met the academic during a campus visit.
“[Mr. Bezos] is a very smart guy. He has a crazy laugh,” said Mr. Guestrin, now UW’s Amazon Professor of Machine Learning. “We got quickly into technical things: What was I working on in large-scale machine learning? How could I impact Amazon? What could this mean for the business of data?”
Google Inc., Facebook Inc., Amazon and other technology companies are scrambling to push the bounds of artificial intelligence, or AI, and in that effort they are stocking their own research centers with big-name academics and aspiring Ph.D. candidates.
Tech companies also are pouring funds into universities with expertise in the once-obscure field. University of Washington, based in the same state as Microsoft Corp. and Amazon, has long been a center of excellence for computer science, including artificial intelligence. Microsoft, Intel Corp. and Google, as well as Amazon, all fund some of UW’s AI research.
UW also has become a Silicon Valley hunting ground. Before it recruited Mr. Guestrin—who earned his reputation creating artificial-intelligence-related tools for developers—the university lost seven AI-related professors to Google.
“There’s a massive battle under way for talent,” said Oren Etzioni,
on leave from UW’s computer-science faculty and now heading up the
Seattle-based Allen Institute for Artificial Intelligence, a nonprofit
set up by Microsoft co-founder Paul Allen.
“Virtually every professor at the UW computer-science department has
been called many times to work at these companies, and frankly it’s a
very compelling pitch.”
Companies are on the prowl not just for big names in the field, but for newly minted Ph.D.s. Amazon is advertising for more than 50 AI positions in the U.S. and Europe, hunting for doctorate-holders in fields like machine learning, information science and statistics.
Last year, Google bought DeepMind, a startup founded by Cambridge University graduates. After the Google deal, DeepMind absorbed two Oxford University spin-offs specializing in AI. As part of the transaction, Google agreed to a research partnership with Oxford’s computer-science program.
Google and Amazon declined to comment about their AI ambitions.
AI is a broad academic field, encompassing techniques aimed at giving computers the ability to make decisions that a human might, based on data analysis. Machine learning and other subsets are a more-targeted discipline inside the broader AI field.
Commercial uses for AI are still limited. Predictive text and Siri, the iPhone’s voice-recognition feature, are early manifestations. But AI’s potential has exploded as the cost of computing power drops and as the ability to collect and process data soars. Big tech companies like Facebook and Google now vacuum up the huge amount of data that needs to be processed to help machines make “intelligent” decisions.
“AI has become ‘like wow,’ in Silicon Valley today,” said Akli Adjaoute, founder and CEO of Brighterion, a software company that uses machine learning techniques to spot financial fraud for credit card customers.
Microsoft is working on understanding context in human interaction.
The company has been awarded a patent for Internet-connected glasses that can detect and interpret the emotions
of people within their field of vision in real time and provide
feedback to the wearer. The patent for “a wearable emotion detection
feedback system,” was filed in October 2012, and awarded this Tuesday.
Asked about Google’s top priorities at a conference last week Executive Chairman Eric Schmidt said the “core thing” his company is working on these days is machine learning. He cited progress in image and speech recognition. Regarding the latter, he said it is a “sore point” that Apple Inc. ’s Siri “gets all the credit.”
The relationship between tech giants and academia can be difficult to navigate. Some faculty members complain tech companies aren’t doing enough in the many collaborative efforts now under way. One big gripe: Companies aren’t willing to share the vast data they are able to collect.
“The high value of this work encourages companies like Google to keep their progress more secret,” said Tom Mitchell, a department head at Carnegie Mellon’s computer-science program.
Those who embrace the relationship say it can provide real-world incentive for scientific advances. Hank Levy, head of UW’s computer-science program, said he isn’t bitter about the poaching from Google over the years.
“Often, people go off for a year or two and then they come back and bring new experiences that expand both their teaching and research,” he said.
In late 2013, Facebook hired Yann LeCun, one of the world’s most prominent AI academics, from New York University. As an AT&T engineer in the 1980s and ’90s, he helped pioneer handwriting-recognition processing used by banks to authenticate checks. He is now Facebook’s chief of artificial intelligence.
As part of the courtship, Facebook let him keep his post at NYU, a block up Broadway from Facebook headquarters. He still works for the university part time. Facebook partnered with the university on a new center dedicated to data science, a key element of AI research. Facebook scientists lecture at NYU, and NYU Ph.D. students can apply for long-term internships at Facebook’s AI lab.
Facebook Chief Executive Mark Zuckerberg read some of Mr. LeCun’s papers before meeting him during the recruitment process. “That completely floored me,” says Mr. LeCun.
—Rolfe Winkler in San Francisco contributed to this article.
When the University of Washington’s computer-science department wanted to poach artificial-intelligence expert Carlos Guestrin from Carnegie Mellon, it turned to Amazon.com Inc.
The Seattle-based tech giant ponied up $2 million to fund two professorships: one for Mr. Guestrin, and another for his wife, who also works in the field. To seal the deal, Amazon Chief Executive Jeff Bezos met the academic during a campus visit.
“[Mr. Bezos] is a very smart guy. He has a crazy laugh,” said Mr. Guestrin, now UW’s Amazon Professor of Machine Learning. “We got quickly into technical things: What was I working on in large-scale machine learning? How could I impact Amazon? What could this mean for the business of data?”
Google Inc., Facebook Inc., Amazon and other technology companies are scrambling to push the bounds of artificial intelligence, or AI, and in that effort they are stocking their own research centers with big-name academics and aspiring Ph.D. candidates.
Tech companies also are pouring funds into universities with expertise in the once-obscure field. University of Washington, based in the same state as Microsoft Corp. and Amazon, has long been a center of excellence for computer science, including artificial intelligence. Microsoft, Intel Corp. and Google, as well as Amazon, all fund some of UW’s AI research.
UW also has become a Silicon Valley hunting ground. Before it recruited Mr. Guestrin—who earned his reputation creating artificial-intelligence-related tools for developers—the university lost seven AI-related professors to Google.
Containers move along conveyors at the Amazon.com Inc. fulfillment center in Tracy, Calif.
Photo:
David Paul Morris/Bloomberg News
Companies are on the prowl not just for big names in the field, but for newly minted Ph.D.s. Amazon is advertising for more than 50 AI positions in the U.S. and Europe, hunting for doctorate-holders in fields like machine learning, information science and statistics.
Last year, Google bought DeepMind, a startup founded by Cambridge University graduates. After the Google deal, DeepMind absorbed two Oxford University spin-offs specializing in AI. As part of the transaction, Google agreed to a research partnership with Oxford’s computer-science program.
Google and Amazon declined to comment about their AI ambitions.
AI is a broad academic field, encompassing techniques aimed at giving computers the ability to make decisions that a human might, based on data analysis. Machine learning and other subsets are a more-targeted discipline inside the broader AI field.
Commercial uses for AI are still limited. Predictive text and Siri, the iPhone’s voice-recognition feature, are early manifestations. But AI’s potential has exploded as the cost of computing power drops and as the ability to collect and process data soars. Big tech companies like Facebook and Google now vacuum up the huge amount of data that needs to be processed to help machines make “intelligent” decisions.
“AI has become ‘like wow,’ in Silicon Valley today,” said Akli Adjaoute, founder and CEO of Brighterion, a software company that uses machine learning techniques to spot financial fraud for credit card customers.
‘The high value of this work encourages companies like Google to keep their progress more secret.’
Asked about Google’s top priorities at a conference last week Executive Chairman Eric Schmidt said the “core thing” his company is working on these days is machine learning. He cited progress in image and speech recognition. Regarding the latter, he said it is a “sore point” that Apple Inc. ’s Siri “gets all the credit.”
The relationship between tech giants and academia can be difficult to navigate. Some faculty members complain tech companies aren’t doing enough in the many collaborative efforts now under way. One big gripe: Companies aren’t willing to share the vast data they are able to collect.
“The high value of this work encourages companies like Google to keep their progress more secret,” said Tom Mitchell, a department head at Carnegie Mellon’s computer-science program.
Those who embrace the relationship say it can provide real-world incentive for scientific advances. Hank Levy, head of UW’s computer-science program, said he isn’t bitter about the poaching from Google over the years.
“Often, people go off for a year or two and then they come back and bring new experiences that expand both their teaching and research,” he said.
In late 2013, Facebook hired Yann LeCun, one of the world’s most prominent AI academics, from New York University. As an AT&T engineer in the 1980s and ’90s, he helped pioneer handwriting-recognition processing used by banks to authenticate checks. He is now Facebook’s chief of artificial intelligence.
As part of the courtship, Facebook let him keep his post at NYU, a block up Broadway from Facebook headquarters. He still works for the university part time. Facebook partnered with the university on a new center dedicated to data science, a key element of AI research. Facebook scientists lecture at NYU, and NYU Ph.D. students can apply for long-term internships at Facebook’s AI lab.
Facebook Chief Executive Mark Zuckerberg read some of Mr. LeCun’s papers before meeting him during the recruitment process. “That completely floored me,” says Mr. LeCun.
—Rolfe Winkler in San Francisco contributed to this article.
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