A Technology Broker and Management Company Specializing in KM Systems
Favorite Today AWS announced new ways for you to easily add machine learning (ML) predictions to applications and business intelligence (BI) dashboards using relational data in your Amazon Aurora database and unstructured data in Amazon S3, by simply adding a few statements to your SQL (structured query language) queries and
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Shared by AWS Machine Learning November 26, 2019
Favorite Organizations in all industries have a large number of physical documents. It can be difficult to extract text from a scanned document when it contains formats such as tables, forms, paragraphs, and check boxes. Organizations have been addressing these problems with Optical Character Recognition (OCR) technology, but it requires
Read More Shared by AWS Machine Learning November 26, 2019
Favorite Machine learning (ML) is routinely used in every sector to make predictions. But beyond simple predictions, making decisions is more complicated because non-optimal short-term decisions are sometimes preferred or even necessary to enable long-term, strategic goals. Optimizing policies to make sequential decisions toward a long-term objective can be learned
Read More Shared by AWS Machine Learning November 26, 2019
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
Read More 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