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    1. Customer Service in a World of Ambient Computing – The Service Center View

      Tweet A few weeks ago I wrote an article about customer service in a world of ambient computing . This article looked at customer service from a customer’s point of view. In it I described how I see customer service getting humanized again by leveraging the advances in AI technologies like Natural Language Processing, speech-to-text- and text-to-speech generation along with intent determination.

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      Mentions: FAQ
    2. Machine Learning – It’s About Technique

      Machine Learning – It’s About Technique

      Tweet When it comes to machine learning, one size does not fit all. Different algorithms, and different techniques within those algorithms, are used to build a model that is application appropriate. But how do you determine which technique is best? Because machine learning is not a concrete set of algorithms used across the board, it depends on what you are trying to achieve. The answer heavily relies on the type of data, and the amount of data, that is available.

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    3. 4 Tips to Avoid the Customer Service Mistakes I Made 10 Years Ago

      4 Tips to Avoid the Customer Service Mistakes I Made 10 Years Ago

      Tweet I had been managing a support team for a few years when my employer partnered with an outside group on a new business venture called Phone.com. In 2007, in the midst of the Global Financial Crisis, any prospect of new business was welcome. My boss approached me and said, “Hey Jeremy, you might start seeing calls, emails, and chats trickle in about this new VOIP phone service.

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      Mentions: Inbenta
    4. The What and Where of Big Data: A Data Definition Framework

      The What and Where of Big Data: A Data Definition Framework

      Tweet I recently read a good article on the difference between structured and unstructured data . The author defines structured data as data that can be easily organized. As a result these type of data are easily analyzable. Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner. Unstructured data are not easy to analyze. A primary goal of a data scientist is to extract structure from unstructured data.

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    5. Actionable Insights: Can Data Analysis Software Deliver Them?

      Actionable Insights: Can Data Analysis Software Deliver Them?

      Tweet When it comes to making sense of data, getting actionable insights is the holy grail. But what does this even mean? When is a finding an insight? When is an insight actionable? Can data analysis deliver them? Let’s get to the bottom of this by looking at some examples. Imagine, you have conducted a survey of 100,000 students, and you seek actionable insights for what to improve at a university. Non-insightful vs. Insightful Knowledge Non-insightful is everything that’s old news to you.

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    6. SDL Brings Neural MT to its Secure Enterprise Translation Server

      Tweet SDL ETS 7.4 covers dozens of language pairs and 18+ industries WAKEFIELD, MA and MAIDENHEAD, UK – June 12, 2017 – SDL today announced that its Neural Machine Translation (NMT) technology is now part of SDL Enterprise Translation Server 7.4 ( SDL ETS ), a secure Machine Translation (MT) platform for regulated industries. Deployed on premise or in a private cloud environment, SDL ETS is designed for companies facing tight regulations, and offers total control of MT-related data.

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    7. If humans want a “Human Touch,” how will AI in CX succeed?

      If humans want a “Human Touch,” how will AI in CX succeed?

      Tweet According to the Chinese calendar, 2016 was the year of the monkey, but in technological terms, it felt more like the year of a silicon-based animal named “Chat Bot.” And 2017 is shaping up to be the year of an even more amorphous but evolving creature nicknamed, AI – Artificial Intelligence. The level of hype in the marketplace over AI continues to climb, with no peak in sight.

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    8. ECommerce is Dead. Long Live iCommerce!

      ECommerce is Dead. Long Live iCommerce!

      Over the past two decades or so eCommerce has undergone a major change. The speed of this change has been, and continues to be, amazing. A Short History The world wide web got invented in 1989, became available on the Internet in 1991; first web sites humbly started as additional marketing channels, with corporate web sites giving information on the company and a product catalogue; 1994 the Netscape browser arrived. In 1995 Amazon got founded – and the story started to take off.

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    9. How AI can improve customer experiences

      How AI can improve customer experiences

      “The future is already here – it’s just not evenly distributed.” –William Gibson, quoted in The Economist, Dec. 4, 2003 For the past couple of months, I’ve really enjoyed using Alexa, thanks to a Christmas gift of a new Amazon Echo. With remarkably good voice recognition, even in a noisy room, Alexa plays music, checks the weather, hails an Uber ride, and adds items to a shopping list. Bonus: tells bad jokes. Amazon Echo And that just scratches the surface.

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD