How businesses are using AI: An interactive guide

77% of companies using #cognitive tech and #AI, use them to innovate products and services.

  • Today’s most advanced cognitive technology users offer a glimpse into the possibilities and tangible benefits of creating intelligent businesses.
  • Did you know that 77% of organizations that already implemented cognitive and artificial intelligence technologies use them to innovate products and services, more than for any other business goal?
  • We looked closely at a subset of advanced users — organizations using two or more cognitive technologies for at least one year.
  • These cognitive pioneers are implementing and increasing adoption of these technologies throughout their organizations, uncovering new use cases and paving a path for others to follow or take in entirely new directions.
  • Cognitive and AI technologies are already driving real business value – – Many of these advanced users are embedding a full spectrum of AI technologies — including machine learning, natural language processing and more — into their processes and products.

Today’s most advanced cognitive technology users offer a glimpse into the possibilities and tangible benefits of creating intelligent businesses.

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AI is revolutionizing neuromarketing

AI is revolutionizing neuromarketing on @venturebeat  #INBOUND17 #ai #ml

  • In recent years, AI has offered a huge boost to neuromarketing — the science of reading consumers’ minds to gauge their reactions to marketing stimuli.
  • When combined with AI-based facial recognition, biometric insights can offer even more accurate data about consumers’ responses to marketing messaging.
  • IBM’s Watson — the company’s computer system that answers questions delivered in natural language — doesn’t use biometric data to gauge the excitement at an event.
  • What makes these methods more than merely interesting is the fact that they provide the means to obtain data about consumers’ emotional reactions in real time and adjust marketing messages accordingly.
  • Once AI is fully adopted in neuromarketing processes, marketers will have the ability to call on consumers to crowdsource their marketing campaigns without the fear of collecting unreliable data.

An unfortunate fact about humanity is that people lie. While this is a chronic issue for human relations, it’s one that may be less of an issue for marketers of the future, thanks to non-human intervention.
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Train Neural Machine Translation Models with Sockeye

New on the AWS #AI Blog: Train Neural Machine Translation Models with Sockeye.

  • Sockeye, which is built on Apache MXNet, does most of the heavy lifting for building, training, and running state-of-the-art sequence-to-sequence models.
  • Sockeye provides both a state-of-the-art implementation of neural machine translation (NMT) models and a platform to conduct NMT research.
  • You can easily change the basic model architecture, including the following elements:

    Sockeye also supports more advanced features, such as:

    For training, Sockeye gives you full control over important optimization parameters.

  • If you have a GPU available, install Sockeye for CUDA 8.0 with the following command:

    To install it for CUDA 7.5, use this command:

    Now you’re all set to train your first German-to-English NMT model.

  • You also learned how to use Sockeye, a sequence-to-sequence framework based on MXNet, to train and run a minimal NMT model.

Have you ever wondered how you can use machine learning (ML) for translation? With our new framework, Sockeye, you can model machine translation (MT) and other sequence-to-sequence tasks. Sockeye, which is built on Apache MXNet, does most of the heavy lifting for building, training, and running state-of-the-art sequence-to-sequence models.
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Lecture Collection

Lecture Collection - Natural Language Processing with #DeepLearning (Winter 2017) [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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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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