A Technology Broker and Management Company Specializing in KM Systems
Favorite Businesses are increasingly developing per-user machine learning (ML) models instead of cohort or segment-based models. They train anywhere from hundreds to hundreds of thousands of custom models based on individual user data. For example, a music streaming service trains custom models based on each listener’s music history to personalize
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Shared by AWS Machine Learning November 26, 2019
Favorite All projects deliver not just a product, but knowledge as well, and there needs to be a clear understanding of what form that knowledge will take. Part of any Knowledge Management policy therefore has to be a definition of the expected knowledge output from project work. This knowledge output
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Shared by Nick Milton November 26, 2019
Favorite Today, Amazon Web Services (AWS) announced Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own specialized machine learning (ML) based image analysis capabilities to detect unique objects and scenes integral to their specific use case. For example, customers using Amazon Rekognition
Read More Shared by AWS Machine Learning November 25, 2019
Favorite All voices are unique, yet speakers tend to adjust their delivery, or speaking style, according to their context and audience. Before Amazon Polly used Neural Text-to-Speech technology (NTTS) to build voices, TTS (Standard Text-to-Speech) voices couldn’t change their speech patterns to match any particular speaking style. When Amazon Polly
Read More Shared by AWS Machine Learning November 25, 2019
Favorite We recently announced that Amazon Transcribe now supports transcription for audio and video for 7 additional languages including Gulf Arabic, Swiss German, Hebrew, Japanese, Malay, Telugu, and Turkish languages. Using Amazon Transcribe, customers can now take advantage of 31 supported languages for transcription use cases such as improving customer service, captioning and subtitling,
Read More Shared by AWS Machine Learning November 25, 2019
Favorite This is a reprise and rewrite of a post from 5 years ago about KM change models vs KM maturity models. AKA “why KM change is more like spread of a forest fire than the growth of a tree”. Photo from the US National Parks Service The use of
Read More Shared by Nick Milton November 25, 2019
Favorite Does KM need a single technology platform? More likely it needs several technologies. This blog post was prompted by a thread in Stan Garfield’s SIKM community asking what technology platform people use for KM. My immediate thought was that a single platform probably is not sufficient. However let’s look
Read More Shared by Nick Milton November 22, 2019
Favorite This is a guest post from Matt Fielder and Jordan Rosenblum at iHeartRadio. In their own words, “iHeartRadio is a streaming audio service that reaches tens of millions of users every month and registers many tens of thousands more every day.” Personalization is an important part of the user
Read More Shared by AWS Machine Learning November 21, 2019
Favorite To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, “Sorry to hear you are having trouble.” Understanding sentiment is also useful in determining when
Read More Shared by AWS Machine Learning November 21, 2019
Favorite If you are a leader who wants to help develop a Knowledge Management and Organisational Learning culture in their organisation, you can do this simply, by asking two questions. I have a question by The US Army on Flickr The two questions are Who have you learned from?Who have
Read More Shared by Nick Milton November 21, 2019