Tag Archives: Learning

Google Gives Wikimedia Millions—Plus Machine Learning Tools
January 23, 2019 12:22 am|Comments (0)

Google is pouring an additional $ 3.1 million into Wikipedia, bringing its total contribution to the free encyclopedia over the past decade to more than $ 7.5 million, the company announced at the World Economic Forum Tuesday. A little over a third of those funds will go toward sustaining current efforts at the Wikimedia Foundation, the nonprofit that runs Wikipedia, and the remaining $ 2 million will focus on long-term viability through the organization’s endowment.

Google will also begin allowing Wikipedia editors to use several of its machine learning tools for free, the tech giant said. What’s more, Wikimedia and Google will soon broaden Project Tiger, a joint initiative they launched in 2017 to increase the number of Wikipedia articles written in underrepresented languages in India, and to include 10 new languages in a handful of countries and regions. It will now be called GLOW, Growing Local Language Content on Wikipedia.

It’s certainly positive that Google is investing more in Wikipedia, one of the most popular and generally trustworthy online resources in the world. But the decision isn’t altruistic: Supporting Wikipedia is also a shrewd business decision that will likely benefit Google for years to come. Like other tech companies, including Amazon, Apple, and Facebook, Google already uses Wikipedia content in a number of its own products. When you search Google for “Paris,” a “knowledge panel” of information about the city will appear, some of which is sourced from Wikipedia. The company also has used Wikipedia articles to train machine learning algorithms, as well as fight misinformation on YouTube.

Even efforts like GLOW—which will now expand to Indonesia, Mexico, and Nigeria, as well as the Middle East and North Africa—can help Google’s own bottom line. When the initiative first launched in India, Google provided Chromebooks and internet access to editors, while the Centre for Internet and Society and the Wikimedia India Chapter organized a three-month article writing competition that resulted in nearly 4,500 new Wikipedia articles in 12 different Indic languages. Smartphone penetration in India is only around 27 percent; as more people in the country start using Android smartphones and Google Search, those articles will make the tech giant’s products more useful. Wikipedia’s blog post announcing Google’s new investment makes this strategy fairly clear, noting that the company also provided Project Tiger with “insights into popular search topics on Google for which no or limited local language content exists on Wikipedia.”

Google is also providing Wikipedia free access to its Custom Search API and its Cloud Vision API, which will help the encyclopedia’s volunteer editors more easily cite the facts they use. Each time a Wikipedia editor adds a new piece of information to an article, they need to cite the source where they learned it. The Search API will allow them quickly look up sources on the web without having to leave Wikipedia, while the vision tool will let editors automatically digitize books so they can be used to support Wikipedia articles too. Earlier this month, Wikimedia also announced Google Translate was coming to Wikipedia, allowing editors to convert content into 15 additional languages, bringing the total available to 121.

These machine learning tools will absolutely make it easier for Wikipedia to reach people who speak languages currently underrepresented on the web. But the encyclopedia is also the reason many AI programs exist in the first place. For example, Google-owned Jigsaw has used Wikipedia, in part, to train its open source troll-fighting AI. The encyclopedia is also used by hundreds of other AI platforms, particularly because every Wikipedia article is under Creative Commons—meaning it can be reproduced for free without copyright restrictions. Apple’s Siri and Amazon’s Alexa smart assistants use information from Wikipedia to answer questions, for instance. (Both companies also have donated to the Wikimedia Foundation as well.)

Google’s new investments in Wikipedia, specifically in GLOW, will address a genuine problem. The majority of Wikipedia’s tens of millions of articles are in English or European languages like French, German, and Russian. (There are also lots of articles in Swedish and two versions of Filipino, but most of these pages were created by a prolific bot). As the estimated half of Earth’s population that still lacks an internet connection comes online, it will be important that reliable information is available in the native languages people speak. That doesn’t mean, though, that in helping solve these issues companies like Google—or Facebook—don’t also have something to gain.


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HSBC: Why machine learning is accelerating cloud adoption
May 9, 2017 6:10 pm|Comments (0)

The financial sector may be one of the more cautious industries when it comes to adopting the cloud. But for HSBC the ability to analyse large volumes of information and access machine learning tools via APIs has served as a catalyst for its own cloud ambitions.

Banks are, by their nature, data-intensive organisations, and HSBC — one of the world’s largest banks, with 37 million customers and billions of dollars in assets — is certainly no exception.

The 150-year old lender has around 100 petabytes of information across its organisation, and that figure is growing fast, too, as customers change the way they bank, favouring digital interactions over traditional methods.

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Cloud and data center trends roundup 2016: Machine learning, hybrid cloud and Google's …
December 30, 2016 2:30 pm|Comments (0)

… the year before. As 2016 draws to a close, Computerworld UK takes a look back at some of the key emerging trends in cloud computing this year.


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What Are The Differences Between AI, Machine Learning, NLP, And Deep Learning?
October 13, 2016 7:00 am|Comments (0)

What is the difference between AI, Machine Learning, NLP, and Deep Learning? This question was originally answered on Quora by Dmitriy Genzel.


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‘Pepper’ robot uses trial-and-error learning to master a child’s game
September 28, 2016 9:43 am|Comments (0)


Robots, much like children, are now using simple games to learn important skills. Pepper, SoftBank’s adorable humanoid robot, recently learned to play ball-in-a-cup (also called ring and pin) in an effort to better understand optimal trajectory. In the beginning, SoftBank’s team demonstrated the game to the robot by guiding its arm. By the end of the video below, Pepper no longer required their assistance. It took 100 tries, but each failed attempt left Pepper to analyze and try to improve upon past performance, much like a human. After each failed attempt, the robot attempted to alter the movement slightly in an…

This story continues at The Next Web


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Machine Learning Breaks Bottleneck In Gathering Valuable Information About Cancer
May 2, 2016 12:00 pm|Comments (0)

Machine learning applied to free-text pathology reports is shown to break a bottleneck that interferes with the process of turning electronically gathered clinical records into information that has widespread value in the healthcare industry.


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IDG Contributor Network: Learning from the PaaS
March 10, 2016 1:20 pm|Comments (0)

In 2009, the U.S. National Institute of Standards and Technology (NIST) published a draft definition of cloud computing. The NIST definition, created for government agencies buying cloud services, simplified cloud computing into three types of services: 1) IaaS (Infrastructure as a Service), where vendors offer on-demand compute, network, and storage; 2) PaaS (Platform as a Service), where vendors provide application development frameworks and deployment tools; and 3) SaaS (Software as a Service) where vendors deliver entire applications.

While IaaS and SaaS are well understood and widely adopted, PaaS has lagged behind. But I expect a massive surge in platform adoption in the next few years.

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Xero leans on Amazon for machine learning and to crack China
March 7, 2016 5:55 am|Comments (0)

Cloud computing giants Amazon, Google and Microsoft are in an arms race to build or buy the best machine learning assets, which help them and …
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IDG Contributor Network: Tidemark goes verticals, machine learning and benchmarking
October 24, 2015 4:25 pm|Comments (0)

Tidemark delivers enterprise performance management (EPM) software. What that esoteric acronym means is that Tidemark helps organizations take internal data they already have and use it to plan the future steps they will take, but also to assess the historical performance of their organization. Tidemark was founded only a few short years ago (in 2009, to be precise) but has already raised close to $ 120 million from a host of investors over multiple rounds. Tidemark is a good example of a new breed of cloud vendor, those that were born into a world already comfortable with cloud-based enterprise tools such as Salesforce and NetSuite. Because of this fact, Tidemark hasn’t had to invent a category; rather it has the somewhat easier job of delivering an existing product category but in new and beneficial ways.

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IDG Contributor Network: Tidemark goes verticals, machine learning and benchmarking
October 10, 2015 9:30 pm|Comments (0)

Tidemark delivers enterprise performance management (EPM) software. What that esoteric acronym means is that Tidemark helps organizations take internal data they already have and use it to plan the future steps they will take, but also to assess the historical performance of their organization. Tidemark was founded only a few short years ago (in 2009, to be precise) but has already raised close to $ 120 million from a host of investors over multiple rounds. Tidemark is a good example of a new breed of cloud vendor, those that were born into a world already comfortable with cloud-based enterprise tools such as Salesforce and NetSuite. Because of this fact, Tidemark hasn’t had to invent a category; rather it has the somewhat easier job of delivering an existing product category but in new and beneficial ways.

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