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Building a Speaker Recognition Model

Domino Data Lab

Unlike fingerprint detection, retinal scans or face recognition, speaker recognition just uses a microphone to record a person’s voice thereby circumventing the need for expensive hardware. Overview of a speaker verification system: Speaker verification is a subfield within the broader Speaker recognition task.

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Women IT leaders take center stage

CIO Business Intelligence

But they also brought her increased recognition which helped her land a job, in 2003, as director of the Dow Jones project management office. You may get a pat on the back, and the recognition might last 10 minutes. You may get a pat on the back, and the recognition might last 10 minutes. First, you serve as a role model.

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The most valuable AI use cases for business

IBM Big Data Hub

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology.

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Must-Have AI Features for Your App

Sisense

Whether it’s core to the product, as with a stock market forecasting algorithm in Quants, or a peripheral component, such as a healthcare domain chatbot that diagnoses diseases via dialog with a patient, building reliable AI components into products is now part of the learning curve that product teams have to manage. .

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Threepeat: Alation Named to Inc. Best Workplaces for Third Time

Alation

After the pandemic sparked months of chaos and closures, and a complete change in our lives, the world collectively shifted to a work-from-home model that has become the norm for most of us. This recognition is so meaningful because it’s based on employee input. magazine’s annual list of the Best Workplaces for the third time!

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Deriving Value from Data Lakes with AI

Sisense

This is both a daunting challenge and an inspiring opportunity, since effective use of AI and ML can cut through about 80% of data preparation (the annoying, routine stuff), leaving humans to handle the remaining 20%, the actual modeling and optimization. Use AI to tackle huge datasets. Let’s talk about AI and machine learning (ML).

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Addressing Irreproducibility in the Wild

Domino Data Lab

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer ’s “ The Ingredients of a Reproducible Machine Learning Model ” talk at a recent WiMLDS meetup. Chloe Mawer presented “ The Ingredients of a Reproducible Machine Learning Model “ talk at a recent WiMLDS meetup.