オープンデータとプログラミング

Open Data

Open Data

深い学習 を、データの豊富な金持ちになります。|With deep learning, the data-rich get richer

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オープンデータ関連のニュースです。

http://www.computerworld.com/article/3181736/internet-of-things/with-deep-learning-the-data-rich-get-richer.html

日本語

以前の記事で企業の深い学習の有望なアプリケーションが話し合った深い学習の最大の可能性は、画像、音声などの少ない構造、意味のようなデータおよび他のセンサー データにビジネスに関連する構造を追加します。 どのように迅速がトーンとサポートの影響はイライラした顧客の変更は、サポート担当者によって分解からコールですか。その時間-する-なんか生音データではなく、あなたのビジネスに重要です。 一般に機械学習アルゴリズムを訓練するとき (と深い網はこれの極端な例) より多くのデータより。「フィット」あなたのデータ – トレーニング セットにしかし不十分な新しいデータに非常によく実行するの永続的な危険性があります。
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English

In a previous post I discussed the promising applications for deep learning in the enterprise. The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data — such as images, audio and other sensor data. How quickly does the tone and affect of a support call from a frustrated customer change, broken down by support rep? It’s that time-to-mollification that matters to your business, not the raw sound data. Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. There’s a persistent danger of “overfitting” your data — performing very well on the training set, but poorly on new data.
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Open Data

Fpga は、次世代の 深い学習 を加速する上で Gpu を打つことができるか。|Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Learning?

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オープンデータ関連のニュースです。

https://www.nextplatform.com/2017/03/21/can-fpgas-beat-gpus-accelerating-next-generation-deep-learning/

日本語

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English

Continued exponential growth of digital data of images, videos, and speech from sources such as social media and the internet-of-things is driving the need for analytics to make that data understandable and actionable. Data analytics often rely on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer state-of-the-art accuracies for important image classification tasks and are becoming widely adopted. At the recent International Symposium on Field Programmable Gate Arrays (ISFPGA), Dr. Eriko Nurvitadhi from Intel Accelerator Architecture Lab (AAL), presented research on Can FPGAs beat GPUs in Accelerating Next-Generation Deep Neural Networks.
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Open Data

中国政府は人工知能の開発に緑色の光を与える|China Government Gives the Green Light to Development of Artificial Intelligence

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オープンデータ関連のニュースです。

http://www.trendintech.com/2017/03/21/china-government-gives-the-green-light-to-development-of-artificial-intelligence/

日本語

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English

The Chinese government has recently announced its intentions to prioritize the development of artificial intelligence (AI) as part of its goal t become the world leaders in this field. Analysts have estimated that the technology industry is set to grow by more than 50 percent over the next 12 months, making it worth around $5.5 billion. In his speech at the annual parliamentary session in Beijing last week, Premier Li Keqiang advised, “We will implement a comprehensive plan to boost strategic emerging industries.  We will accelerate research and development (R & D) on, and the commercialization of new materials, artificial intelligence, integrated circuits, bio-pharmacy, 5G mobile communications, and other technologies.
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Open Data

deepsense.io CEO: Cloudera のベンダー ロックの代わりを使用してネプチューンをなぜ許可するか?|deepsense.io CEO: Why Allow Cloudera’s Vendor Locking Instead of Using Neptune?

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オープンデータ関連のニュースです。

http://finance.yahoo.com/news/deepsense-io-ceo-why-allow-180600957.html

日本語

パロ ・ アルト、カリフォルニア、2017 年 3 月 21 日日/prnewswire/- San Jose の層 + Hadoop 世界で Cloudera 発表彼らの新しいツールは現在ベータ版で利用可能です。Cloudera データ科学ワークベンチの機能のほとんどと同じネプチューンのデータ科学者たち、彼らの作業効率を向上させるための deepsense.io の DevOps プラットフォームであると機械学習になり実験管理と展開の迅速かつ容易に、コア活動のための時間を節約することができます – 思考。 トマシュ Kulakowski、CEO の deepsense.io は本日述べた:”データ科学分野の成長サポート データ科学者の日常的な活動のツールありますより多く参照してくださいに満足しております。
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English

PALO ALTO, California, March 21, 2017 /PRNewswire/ — At Strata+Hadoop World in San Jose, Cloudera announced their new tool currently available in beta. Most of the Cloudera Data Science Workbench’s features are the same as Neptune’s, which is deepsense.io’s DevOps platform for data scientists, helping them increase their work efficiency, and thereby making machine learning experiments management and deployment faster and easier, allowing to save time for their core activity – thinking. Tomasz Kulakowski, CEO of deepsense.io said today: “We are happy to see that the data science sector grows and that there are more and more tools supporting data scientists’ day-to-day activities.
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Open Data

IBM と PlayFab 使用 機械学習 してプレイヤーの振る舞いを理解します。|IBM and PlayFab will use machine learning to understand player behaviour

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オープンデータ関連のニュースです。

http://www.gamasutra.com/view/news/294137/IBM_and_PlayFab_will_use_machine_learning_to_understand_player_behaviour.php

日本語

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English

Computing giant IBM and backend service provider PlayFab have formed a joint initiative to give game developers deeper insights into player behavior. The program will let developers feed data into IBM’s Watson artificial intelligence platform for analysis, with the system capable of building models from structured and unstructured data, as well as open machine learning libraries. Watson will use that information to deliver lessons and feedback directly to devs through PlayFab, allowing them to make meaningful changes to their games in a bid to boost engagement by better understanding players.
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Open Data

インテリジェントなクラウドのインとアウト|​The ins and outs of the intelligent cloud

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オープンデータ関連のニュースです。

http://www.arnnet.com.au/article/616224/ins-outs-intelligent-cloud/

日本語

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English

Cloud-based artificial intelligence and machine learning is on the cusp of providing partners with new opportunities, but barriers remain. In January, IBM announced that it was merging the artificial intelligence [AI] tools of fellow tech powerhouse, Google, with its own cognitive computing technologies. The aim of the move is to allow its deep-learning systems to more accurately come up with answers for thorny questions. Meanwhile, also at the turn of the year, Apple became the latest tech company to sign up to the Partnership on AI alliance, joining rivals such as Google, IBM, Microsoft, and Facebook to help advance research and development of AI technologies.
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Open Data

人生と生活を作り直す技術|The Technologies Reshaping Life and Livelihood

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オープンデータ関連のニュースです。

http://blogs.edweek.org/edweek/on_innovation/2017/03/the_technologies_reshaping_life_and_livelihood.html

日本語

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English

On a recent visit to Energy Institute High School in Houston, students were contemplating the impact of robots and artificial intelligence (AI) on their community. Every high school student should have a similar opportunity to study artificial intelligence and its influence on their life and work. It promises to be the single most important change driver over the next two decades but is seldom discussed on high school campuses. AI–the notion that machines could exhibit human intelligence–was conceived in the 1950s but it became a really big deal with the recent explosion of big data powered by cheap computing and storage (Moore’s Law) and lots of devices, sensors, cameras and RFID tags (the Internet of Things).
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Open Data

NVIDIA 株式非任天堂スイッチ成功によって危険にさらした: アナリスト|NVIDIA Corporation Stock De-Risked By Nintendo Switch Success: Analyst

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オープンデータ関連のニュースです。

http://www.valuewalk.com/2017/03/nvidia-stock-de-risked-nintendo-switch/

日本語

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English

NVIDIA stock surged more than 3% during regular trading hours on Monday as analysts assessed the company’s Deep Learning Connect event, which was held last week. One analyst said on Friday that the better-than-expected sales of the Nintendo Switch will boost NVIDIA’s results, while another said on Monday that last week’s event gave them a fuller understanding of the chip maker’s machine learning and customer engagement efforts. NVIDIA stock has had a volatile 2017 so far despite being up 222% for the last 12 months, but since Intel’s acquisition of autonomous driving firm Mobileye was announced last week, NVIDIA shares have been climbing. NVIDIA stock has rallied by about 9%, snapping a pair of downward trends.
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Open Data

Google チーム リファイン GPU 搭載ニューラル機械翻訳|Google Team Refines GPU Powered Neural Machine Translation

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オープンデータ関連のニュースです。

https://www.nextplatform.com/2017/03/20/google-team-refines-gpu-powered-neural-machine-translation/

日本語

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English

Despite the fact that Google has developed its own custom machine learning chips, the company is well-known as a user of GPUs internally, particularly for its deep learning efforts, in addition to offering GPUs in its cloud. At last year’s Nvidia GPU Technology Conference, Jeff Dean, Senior Google Fellow offered a vivid description of how the search giant has deployed GPUs for a large number of workloads, many centered around speech recognition and language-oriented research projects as well as various computer vision efforts. What was clear from Dean’s talk—and from watching other deep learning shops with large GPU cluster counts—is that the real innovation is around tightening deep learning algorithms and frameworks to hum on GPU clusters.
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Open Data

DNN 人気ドライブ ジェットソンの NVidia の TX2|DNN Popularity Drives NVidia’s Jetson TX2

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オープンデータ関連のニュースです。

http://electronicdesign.com/industrial/dnn-popularity-drives-nvidia-s-jetson-tx2

日本語

深いニューラル ネット (DNNs) と深い学習に関心の高まりを NVidia のジェットソン TX2 のようなプラットフォームの人気運転している (図 1)。87 mm ジェットソン TX2 モジュールによって 50 ミリメートルは、256 コア NVIDIA パスカル GPU、64 ビット NVIDIA デンバー 2 ARM 互換コアと 4 つの 64 ビット腕 A57 コアのペアを持っています。単精度パフォーマンスの 2 TFLOPs を実現します。 ジェットソン TX2 はカフェや Tensorflow のような深い学習フレームワークをサポートすることができます NVidia の CUDA プログラマ環境として cuDNN (CUDA 深いニューラル ネットワーク) プラットフォームをサポートします。 1. NVidia のジェットソン TX2 は 256 コア NVIDIA パスカル GPU パフォーマンスの 2 TFLOPs を実現します。 ジェットソン TX2 では、キャリア ボードを操作する必要があります。NVidia の開発キットも少ないので、展開のためのモジュールをチェック アウトお勧めします。
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English

The increased interest in deep neural nets (DNNs) and deep learning are driving the popularity of platforms like NVidia’s Jetson TX2 (Fig. 1). The 50-mm by 87-mm Jetson TX2 module has a 256-core NVIDIA Pascal GPU, a pair of 64-bit NVIDIA Denver 2 ARM-compatible cores, and four 64-bit ARM A57 cores. It delivers 2 TFLOPs of single precision performance. The Jetson TX2 supports NVidia’s CUDA programmer environment as well as the cuDNN (CUDA deep neural network) platform, allowing it to support deep-learning frameworks like Caffe and Tensorflow. 1. NVidia’s Jetson TX2 has a 256-core NVIDIA Pascal GPU that delivers 2 TFLOPs of performance. The Jetson TX2 requires a carrier board to operate. NVidia’s development kit is good for checking out the module, but less so for deployment.
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