Posts Tagged ‘Facebook’

Declara Individualizes Large-Scale Learning

coggraphLearning at large-scale. That’s the work Declara is undertaking with its CognitiveGraph platform that leverages semantic search, social platforms and predictive analytics to build context-specific learning pathways for the individuals involved in mass learning efforts. Think, for example, of teachers in a country working to re-educate all its educators, or retail and manufacturing workers in parts of the world who need new skill sets because machines have taken on the work these people used to do.

Adults don’t have the luxury of just being focused on learning, so “we try to help them learn more effectively and quickly, using the CognitiveGraph as a way of knowing where to start from and how to get them to positive outcomes faster,” says co-founder and CEO Ramona Pierson. Its intelligent learning platform will determine what mentors and information exist within a closed private network or on the Web relative to supporting a user’s learning needs; what of all that will be the best fit for a particular user; and then match that learner to the best pathway to acquire the new skills. Among the technologies Declara is leveraging is Elasticsearch (which the Semantic Web Blog discussed most recently here) realtime search and analytics capabilities to turn data into insights.

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Microsoft Talks Up What It’s Calling The First Truly Virtual Personal Assistant

Microsoft cortanaAs The Semantic Web Blog discussed yesterday here, the Virtual Personal Assistant is getting more personal. Microsoft officially unveiled Cortana as part of the Windows Phone 8.1 smartphone software at its Build event yesterday, and the service effectively replaces the search function on Windows smartphones, both for the Internet and locally.

This statement served as the theme from corporate vice president and manager Joe Belfiore: “Cortana is the first truly personal digital assistant who learns about me and about the things that matter to me most and the people that matter to me most, that understands the Internet and is great at helping me get things done.”

The Bing-powered Cortana is launching in beta mode, and was still subject to a few hiccups during the presentation. For example, when Belfiore asked Cortana to give him the weather in Las Vegas, it reported the information in degrees, and was able to respond to his request to provide the same information in Celsius. But he couldn’t get her to make the calculations to Kelvin. But, he promised attendees, “Try it yourself because she is smart enough to tell you the answer in Kelvin.”

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Google, Facebook, and the Cold War

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Ron Callari of Inventor Spot recently wrote, “It’s hard to say, looking twenty to thirty years into the future, just how different the digital landscape will look. Semantic Technology, Augmented Reality, Virtual Reality and Web 3.0 are presently only toddling along in their infant stage. What they will look like in the next few decades is only guesswork on our part.  However if we were pressed to gamble on the outcome, a smart man’s wager might be that the last two digital super powers left standing will be Google and Facebook [with the possible exception of China]. A CNN Money report describes this evolution as analogous to the ‘Cold War,’ to conjure up imagery of what transpired between America and the Soviet Union, post World War II.” Read more

Facebook’s DeepFace Matches Faces Almost as Well as Humans

Facebook

Tom Simonite of the MIT Technology Review reports, “Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time. New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera. That’s a significant advance over previous face-matching software, and it demonstrates the power of a new approach to artificial intelligence known as deep learning, which Facebook and its competitors have bet heavily on in the past year.” Read more

Semantic Web Jobs: Facebook

Facebook

Facebook is looking for a Partner Engineer (Data Applications) in Menlo Park, CA. According to the post, “Partner Engineering is a highly technical team that works with our strategic partners to integrate Facebook Platform into their Web sites, applications, and devices. This role demands an in-depth understanding of complex issues related to semantics, data modeling, platform architecture, application development, and management. The ideal candidate will have 15+ years of professional data analysis and systems architecture experience, including both relational database and semantic modeling work.” Read more

Love Is In The Air, And On The Semantic Web

Courtesy: Flickr/by Phillie Casablanca

Courtesy: Flickr/by Phillie Casablanca

Not everyone gets to have quite the affectionate relationship with technology that Joaquin Phoenix has with Samantha in Her. But it’s nearly Valentine’s Day, and so as good a time as any to at least review some of the ways that semantic and related technologies are helping us find — and stay — in love:

  • Graphing relationships is the game at dating app Hinge, which works to connect Facebook friends with friends’ friends, using their history and likes to build a graph about each other that gets the love conversation started. The free mobile data-driven matchmaking app is available in  NYC, DC, Philadelphia, and Boston, and most recently came online in San Francisco, too.
  • Folks in search of romance also have the Freebase-powered LoveFlutter to check into. It, too, makes use of your Facebook interests and extends that with the help of the Freebase’s database to fill out other details about those interests – such as what genre of movies it is that you like – to semantically connect your interests with that of other users, and you with them. It will use that data to suggest a great first date spot for you, too. Costs range from free to $29.99 month.

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Elasticsearch 1.0 Takes Realtime Search To The Next Level

esearchpixElasticSearch 1.0 launches today, combining Elasticsearch realtime search and analytics, Logstash (which helps you take logs and other event data from your systems and store them in a central place), and Kibana (for graphing and analyzing logs) in an end-to-end stack designed to be a complete platform for data interaction. This first major update of the solution that delivers actionable insights in real-time from almost any type of structured and unstructured data source follows on the heels of the release of the commercial monitoring solution Elasticsearch Marvel, which gives users insight into the health of Elasticsearch clusters.

Organizations from Wikimedia to Netflix to Facebook today take advantage of Elasticsearch, which vp of engineering Kevin Kluge says is distinguished by its focus from its open-source start four years ago on realtime search in a distributed fashion. The native JSON and RESTful search tool “has intelligence where when it gets a new field that it hasn’t seen before, it discerns from the content of the field what type of data it is,” he explains. Users can optionally define schemas if they want, or be more freeform and very quickly add new styles of data and still profit from easier management and administration, he says.

Models also exist for using JSON-LD to represent RDF in a manner that can be indexed by Elasticsearch. The BBC World Service Archive prototype, in fact, uses an index based on ElasticSearch and constructed from the RDF data held in a central triple store to make sure its search engine and aggregation pages are quick enough.

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The Supply Chain Is One Big Graph In Start-up Elementum’s Platform

rsz_elementum_transport_appStartup Elementum wants to take supply chains into the 21st century. Incubated at Flextronics, the second largest contract manufacturer in the world, and launching today with $44 million in Series B funding from that company and Lightspeed Ventures, its approach is to get supply chain participants – the OEMs that generate product ideas and designs, the contract manufacturers who build to those specs, the component makers who supply the ingredients to make the product, the various logistics hubs to move finished product to market, and the retail customer – to drop the one-off relational database integrations and instead see the supply chain fundamentally as a complex graph or web of connections.

“It’s no different thematically from how Facebook thinks of its social network or how LinkedIn thinks of what it calls the economic graph,” says Tyler Ziemann, head of growth at Elementum. Built on Amazon Web Services, Elementum’s “mobile-first” apps for real-time visibility, shipment tracking and carrier management, risk monitoring and mitigation, and order collaboration have a back-end built to consume and make sense of both structured and unstructured data on-the-fly, based on a real-time Java, MongoDB NoSQL document database to scale in a simple and less expensive way across a global supply chain that fundamentally involves many trillions of records, and flexible schema graph database to store and map the nodes and edges of the supply chain graph.

“Relational database systems can’t scale to support the types of data volumes we need and the flexibility that is required for modeling the supply chain as a graph,” Ziemann says.

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Google Buys DeepMind Technologies, Growing Its Deep Learning Portfolio And Expertise

deepmindGoogle’s letting the cash flow. Fresh off its $3.2 billion acquisition of “conscious home” company Nest, which makes the Nest Learning Thermostat and Protect smoke and carbon monoxide detector, it’s spending some comparative pocket change — $400 million – on artificial intelligence startup DeepMind Technologies.

The news was first reported at re/code here, where one source describes DeepMind as “the last large independent company with a strong focus on artificial intelligence.” The London startup, funded by Founders Fund, was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman, with the stated goal of combining machine learning techniques and neuroscience to build powerful general purpose learning algorithms.

Its web page notes that its first commercial applications are in simulations, e-commerce and games, and this posting for a part-time paid computer science internship from this past summer casts it as “a world-class machine learning research company that specializes in developing cutting edge algorithms to power massively disruptive new consumer products.”

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Open The Door To Bringing Linked Data To Real-World Projects

ld1Linked Data: Structured Data on the Web is now available in a soft-cover edition. The book, authored by David Wood, Marsha Zaidman, Luke Ruth, and Michael Hausenblas, and with a forward by Tim Berners-Lee, aims to give mainstream developers without previous experience with Linked Data practical techniques for integrating it into real-world projects, focusing on languages with which they’re likely to be familiar, such as JavaScript and Python.

Berners-Lee’s forward gets the ball rolling in a big way, making the case for Linked Data and its critical importance in the web ecosystem:“The Web of hypertext-linked documents is complemented by the very powerful Linked Web of Data.  Why linked?  Well, think of how the value of a Web page is very much a function of what it links to, as well as the inherent value of the information within the Web page. So it is — in a way even more so — also in the Semantic Web of Linked Data.  The data itself is valuable, but the links to other data make it much more so.”

The topic has clearly struck a nerve, Wood believes, noting that today we are “at a point where structured data on the web is getting tremendous play,” from Google’s Knowledge Graph to the Facebook Open Graph protocol, to the growing use of the schema.org vocabulary, to data still growing exponentially in the Linked Open Data Project, and more. “The industry is ready to talk about data and data processing in a way it never has been before,” he continues. There’s growing realization that Linked Data fits in with and nicely complements technologies in the data science realm, such as machine learning algorithms and Hadoop, such that “you can suddenly build things you never could before with a tiny team, and that’s pretty cool….No technology is sufficient in and of itself but combine them and you can do really powerful things.”

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