オープンデータとプログラミング

Author Archive for opendata

Open Data

銀行の競争を促進する緑色の光を データを開く でモリソンのヒントします。|Morrison hints at open data green light to boost banking competition

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オープンデータ関連のニュースです。

http://www.afr.com/news/scott-morrison-hints-at-open-data-green-light-to-boost-banking-competition-20170507-gvzrjo

日本語

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Treasurer Scott Morrison has hinted strongly that the government will give banking customers power over their own data so they can shop around for a better deal from other financial institutions. The change could be announced as early as budget night on Tuesday, at the same time the Treasurer launches a Productivity Commission inquiry into the State of Competition in the Australian Financial System, a process which could lead to sweeping change. There has already been a Productivity Commission inquiry into so-called open data and the government has the final report. The interim report on “data use and availability” sent to the government on March 31 called for customers to be granted a “comprehensive right” to their data held by corporations or governments.
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Open Data

シカゴの RSocrata とパブリック データを保持します。|Preserve Public Data with Chicago’s RSocrata

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オープンデータ関連のニュースです。

http://digital.cityofchicago.org/index.php/archive-open-data/

日本語

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Chicago has rolled-out a public test for a new feature designed to help scientists, journalists, archivists, and others to archive and preserve open data. In light of recent concerns about the removal of government data from public websites and data portals, particularly related to climate change, this new feature allows for data to be downloaded archived with a simple command in the R language. All of the data, including maps, saved to your hard drive and time-stamped so you can easily see when the data was last preserved. The data are also compressed on your hard drive to to save space. In the near future, we will offer the ability to save data to the cloud to make it easier to save very large open data sets.
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Open Data

外資系企業が「植民地化」オーストラリアの銀行|Foreign firms could “colonise” Australian banking

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オープンデータ関連のニュースです。

https://www.investordaily.com.au/markets/41218-foreign-firms-could-colonize-australian-banking

日本語

初心者支払いエグゼクティブ ディレクター水曜日、5 月 3 日フランス オーストラリア商工会議所のメンバーに話す Jost Stollmann だ、世界的に銀行の変容を遂げよう。 「とそれはなくなって、ウィンドウを欠場する場合、windows で起こる変換を参照して今より規制する傾向があるより複雑なビジネス場所でデジタル変換のウィンドウが開き、」と彼は言った。 「個人的に思う銀行は中断ために熟して。」 氏 Stollmann は言ったこの混乱; プレイできる 3 つの方法があります。大手銀行が改革でき、自分自身を混乱させる、現職の選手に挑戦する革新的な新興企業が通って来ることがまたはアリババの Ant 金融などデータ アグリゲーターが金融サービスの支配的な力になる可能性があります。
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Speaking to members of the French-Australian Chamber of Commerce on Wednesday, 3 May, Tyro Payments executive director Jost Stollmann said that globally, banking is on the cusp of transformation. “Transformations happen in windows, if you miss the window it’s gone, and I see that now the window of digital transformation opens in those business places that are more complicated and tend to be more regulated,” he said. “I personally think that banking is ripe for disruption.” Mr Stollmann said there are three ways this disruption could play out; the major banks could reinvent and disrupt themselves, innovative start-ups could come through to challenge the incumbent players, or data aggregators such as Alibaba’s Ant Financial could become the dominant force in financial services.
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Open Data

2017 ケンタッキー ダービーで AI ミスを繰り返しますが、ここで学んだことは、|AI misses repeat in 2017 Kentucky Derby, but here’s what we learned

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オープンデータ関連のニュースです。

http://www.techrepublic.com/article/ai-misses-repeat-in-2017-kentucky-derby-but-heres-what-we-learned/

日本語

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When an artificial intelligence-powered “swarm” correctly predicted the order of the top four finishers in last year’s Kentucky Derby, the surprising story got picked up around the world. For the 2017 Derby, artificial intelligence learned the same lesson that has been beating up dyed-in-the-wool handicappers for over a century: horse racing is the most unpredictable sport on the planet. This year’s swarm only predicted two of the four top finishers—Always Dreaming and Classic Empire—and it had the pair in the wrong order. The swarm picked Always Dreaming fourth while the colt ended up controlling the race and winning easily. Classic Empire was the disputed top selection of the swarm but he barely nabbed fourth place in the race on Saturday.
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Open Data

なぜデータ ストレージは、AI の拡張の次のフロンティア|Why Data Storage is the Next Frontier in AI Expansion

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オープンデータ関連のニュースです。

https://edgylabs.com/2017/05/05/data-storage-ai-nvidia/

日本語

AI 技術の新しい開発があるたびに私たちは最初どのような近代的な人工知能の基礎を行うことができますを説明してどのように動作することがなくそれを説明できないようです。私の部分について、私は甘い科学にそれを持っています。 それはこのような何かを行く: 言葉「深い学習ニューラル ネットワーク’ を言及し AI が学習し、自己を向上させるデータの膨大な量をクランチすることができますを説明します。 今だけ、しかし、私はステートメント、AI が学習し、自分自身を向上させる具体的には一部の後半部分での構築傾向があることに気付いています。それはどのように彼らはそれを行うことができます本当にクールなだが高い時間、私たちは、方程式の ‘データの膨大な量の一部について話し始めるストレージの鋭い焦点なし愛革命が起こることはありませんので。
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It seems like every time there is a new development in AI technology, we cannot explain it without first explaining the basics of what modern AI can do and how they work. For my part, I’ve got it down to a sweet science. It goes something like this: Mention the words ‘deep learning neural network‘, then explain that AI can crunch enormous amounts of data to learn and self-improve. Only now, however, I’m noticing that I tend to build on the latter part of that statement, specifically the part where the AI learns and improves itself. And while it’s really cool how they can do that, it’s high time that we start talking about the ‘enormous amounts of data’ part of the equation, because without a keen focus on storage the AI revolution won’t happen.
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Open Data

Google の最も重要なプロジェクトの 1 つをリードするために退職を残した 69 歳教授に会う|Meet the 69-year-old professor who left retirement to help lead one of Google’s most crucial projects

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オープンデータ関連のニュースです。

http://www.cnbc.com/2017/05/06/googles-tpu-for-machine-learning-being-evangelized-by-david-patterson.html

日本語

1 年前カリフォルニア大学バークレーは、デビッド ・ パターソン、コンピューター アーキテクチャで 40 年間の学術のキャリアの後にハングアップした人の退職お祝いをホストされています。 パターソン encored イベント昨年 5 月個人 16 分歴史、高校や大学とカリフォルニア大学ロサンゼルス校、続いてヒューズ ・ エアクラフトでの仕事、バークレー校の 4 年間で、主要な数学のレスリング選手として彼の日を記録します。 スタンフォード大学のジョン ・ ヘネシーと 2 冊の本を書いて、コンピューティング研究会の議長を務めからパターソン聴衆に語った彼の成功への鍵は「一度に 1 つ大きなもの」をやっていた 彼の次の大きなものは、巨大な可能性があります。
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A year ago the University of California at Berkeley hosted a retirement celebration for David Patterson, who was hanging it up after a 40-year academic career in computer architecture. Patterson encored the event last May with a personal 16-minute history, chronicling his days as a wrestler in high school and college and a math major at UCLA, followed by a job at Hughes Aircraft and four decades at Berkeley. From writing two books with Stanford University’s John Hennessy to chairing the Computing Research Association, Patterson told the audience that a key to his success was doing “one big thing at a time.” His next big thing could be enormous.
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Open Data

多くのソフトウェア エンジニアは、トロントに向かっている一つの理由: トランプ|One reason more software engineers are heading to Toronto: Trump

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オープンデータ関連のニュースです。

http://www.cnbc.com/2017/05/05/vector-institute-ai-engineers-to-head-to-toronto-because-of-trump.html

日本語

数週間だけ出回っているトロントでベクター研究所、シリコン バレーからトップのマシン学習の才能をすでに集めています。 この理由の 1 つ: 代表取締役社長ドナルド ・ トランプ。 ヨルダン ・ ジェイコブス、ベクター研究所の共同創設者「地政学は、一部の再生連絡の人々 が言った ‘私は米国に住んでいるたく’、”CNBC に語った。 歓迎を感じるなど、移民を巡るトランプ政権の最近の戦いは何人かの人々 に行ったより人工知能の研究を行うために米国に移動するをためらう。 “メールと通話を求める人々 がされている: のアプリケーションを送信することができるとき?」ラケル Urtasun、ベクター研究所の共同創設者は話しました。
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The Vector Institute in Toronto has only been around for a few weeks, and it is already attracting top machine learning talent from Silicon Valley. One reason for this: President Donald Trump. “Geopolitics is playing a part, as people contacting us have said ‘I don’t want to live in the U.S.,'” Jordan Jacobs, co-founder of the Vector Institute, told CNBC. The Trump administration’s recent battles over immigration have made some people feel unwelcome, and others more hesitant to move to the U.S. to do research in artificial intelligence. “People have been emailing and calling to ask: When can I send my application?” Raquel Urtasun, co-founder of the Vector Institute, told CNBC.
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Open Data

データのパターン|Patterns in the Data

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オープンデータ関連のニュースです。

https://www.genomeweb.com/scan/patterns-data-0

日本語

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With machine learning, a genomics company is moving into drug development, Technology Review reports. Deep Genomics, which had been focusing on using its machine-learning tools to search for genetic changes that might be causal for certain diseases, is now setting its sights on developing drugs. According to Tech Review, Deep Genomics will first try its hand at developing drugs for Mendelian disorders.
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Open Data

機械学習に役立ちます潜在的切削回転を雇うより、大企業|Machine learning helps large companies hire better, potentially cutting turnover

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オープンデータ関連のニュースです。

http://business.financialpost.com/entrepreneur/0508-biz-dd-hr

日本語

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You might say the folks at Ideal were victims of their own success. Co-founders Somen Mondal and Shaun Ricci’s latest company grew from a challenge they faced in their first venture, inspection and safety compliance management programs. “We started the first company, Field ID, as two people working out of an attic, and grew to the point we were hiring two salespeople a week,” Ideal CEO Mondal says. “We had all the classic HR nightmares: we would hire with bias and ignore résumé details. We had terrible recruiter efficiency and bad quality hires. It was so bad we knew that one of every two hires we made would likely be fired.” Despite the challenges, Field ID was acquired in 2012 by Master Lock for a sizable sum.
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Open Data

AI と爆発の古いコンテキスト|Exploding Old Contexts With AI

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オープンデータ関連のニュースです。

https://www.mediapost.com/publications/article/300606/exploding-old-contexts-with-ai.html

日本語

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According to IBM, we produce 2.5 exabytes of data each day. This is equivalent to 250,000 Libraries of Congress or 90 years of HD video – each day.This data exhaust results from our continual digital interactions, whether explicit – such as typing a search into Google – or implicit, like the location signals we give off as we move through the world with our smartphones. In advertising, we use these data signals for demand capture – which is less expensive than demand generation – usually in programmatic contexts, for targeting and analytics and attribution. From a user’s point of view, much of that data output is visual and text-based, such as a list of Google search results. This visual environment is a good  brand too, since it contextualizes and dimensionalizes its offering.
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