The app is dead. And the IoT killed it. Internet of Things blog

Jane Jetson didn't use #apps - ! #TheJetsons #IoT #AI #H2M

  • Like millions of people around the world, you may have received a shiny new device as a holiday gift at the end of last year.
  • The world has changed for the better because WE NO LONGER NEED A SEPARATE APP to use our things, the ‘things’ in the Internet of Things (IoT).
  • — @Libovness (Source)

    Text is still superior to voice, as anyone who has used Google Home or Amazon Echo (yes, I have one, and I love it) and/or tried to use voice while waiting in an airport security line without having other people overhear: 1) your proprietary business information, 2) your squeeze’s silly nickname, or 3) your holiday weight gain, can tell you.

  • , increase product usability (no apps to learn, even grandmothers can message, especially when they get to choose their own channel), and focus on what they do best (i.e., I have yet to meet a product engineer who loves managing dozens of communications platforms’ constantly changing Application Processing Interfaces (APIs)).
  • Learn more about how UIB is enabling products and software to communicate with people and things, and see the latest smart home, Industry 4.0, and smart city use cases now at unifiedinbox.com and unificationengine.com.

Ken Herron of IoT messaging company Unified Inbox, explores how the IoT means we no longer need a separate app to use our things.
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Top 10 Machine Learning Projects on Github

Top 10 #MachineLearning Projects on Github #KDN

  • The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources.
  • While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community.
  • This is a curated list of machine learning libraries, frameworks, and software.
  • The list is categorized by language, and further by machine learning category (general purpose, computer vision, natural language processing, etc.).
  • This is a collection of IPython notebook tutorials for scikit-learn, as well as a number of links to related Python-specific and general machine learning topics, and more general data science information.


The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.

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Google Sheets now uses machine learning to help you visualize your data

Google Sheets now uses machine learning to help you visualize your data  #CompBindTech

  • After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions about your data, it’s now expanding this feature to also automatically build charts for you.
  • All of this is backed by the same natural language understanding tech that already powered the “Explore” feature.
  • It’s worth noting that the previous version of “Explore” could already build graphs for you, but those focused on your complete data set.
  • With this new version, Google also is making it easier to keep in sync data from Sheets that you use in Docs or Slides.
  • You could already update charts you copy into Docs and Slides with just a click, but now you also can do the same with tables.

Google Sheets is getting smarter today. After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions..
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Lecture Collection

Natural Language Processing with #DeepLearning lecture videos, Manning & Socher (Stanford)

  • Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication.
  • This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation.

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture s…
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Star Trek IBM’s Watson to Power Bridge Crew VR Interactive Speech Experience

IBM's Watson to Power Bridge Crew #VR Interactive Speech Experience  #ai

  • IBM’s Watson will power in-game voice command for Ubisoft’s upcoming release of Star Trek: Bridge Crew during an experimental Beta period later this summer following the game’s launch on May 30.
  • In-game speech experiences, built with IBM Watson for Star Trek: Bridge Crew will be available this summer in Beta for cross-platform play.
  • The Watson and Star Trek: Bridge Crew partnership will allow players to give direct, interactive speech commands to virtual Starfleet shipmates.
  • “For the first time, Watson will power the technology that makes it possible for gamers and fans of Star Trek to interact with the crew,” said Willie Tejada, Chief Developer Advocate, IBM.
  • For more information visit Star Trek: Bridge Crew and IBM VR Speech Sandbox.

Star Trek News – IBM’s Watson to power Bridge Crew VR interactive speech experience and make code available to all developers. Details at… 
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AI Tech Talk: An Overview of AI on the AWS Platform

New on the AWS #AI Blog,

  • AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs.
  • For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
  • For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale.
  • Launch instances of the AMI, pre-installed with open source deep learning frameworks (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
  • Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of the managed AI services, the AI Platform offerings, and the AI Frameworks you can run on the AWS Cloud.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
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How Artificial Intelligence is Changing Web Writing

How #ArtificialIntelligence is Changing Web Writing  #ai

  • In short, in the universe of pages existing in a (at the time almost) shapeless web, Page and Brin wanted to organize that information to make it become knowledge.
  • Out of the more than 200 factors that Google accounts for when deciding whether the content on the web is relevant, RankBrain became the third most relevant.
  • First, the semantic web is a set of rules and standards that make human language readable to machines.
  • In semantic web jargon an entity is a subject which has unambiguous meaning because it has a strong contextual foundation.
  • In other words, instead of going from writing to web writing as unconsciously as the human race transitioned from hunter-gathering to farming, it is time to take this step forward deliberately and intentionally.

When Larry Page and Sergey Brin invented PageRank back in 1996, they had one simple idea in mind: Organize the web based on “link popularity.” In…
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