When planning the development of a chatbot or virtual assistant, it’s important to begin the conversational design process with a clear data strategy in place. Conversational design requires more than just an understanding of the fundamentals of voice user interface design — you also need to understand how to solve some of the key challenges in data preparation. Working with the …
For many organizations, the temptation to annotate data for machine learning (ML) projects in-house is hard to deny. These companies typically feel that using internal resources will help them save time and money by tapping employees who are already on their payroll. Additionally, if their project is highly confidential or of a sensitive nature, they might feel that using internal …
Machine learning requires high volumes of data for training, validation, and testing. A machine learning model learns to find patterns in the input that is fed to it. This input is referred to as training data. As you train your solution to form relationships between variables, it’s important to have the right data, structured in the right format, covering all …
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When designing a voice recognition system, it’s important that the speech system understands everyone speaking to it. But what happens when a speech recognition system doesn’t account for children’s speech? Most speech recognition platforms are only designed with adults in mind. Child speech is very different than adult speech, and not all speech recognition devices are well-equipped to deal with …
Crowdsourcing – best practices and innovation learned at Crowdsourcing Week (CSW) 2017 in Seattle.
Kerri Reynolds, Sr. VP of Human Resources and Crowdsourcing, shares insights into crowdsourcing and crowd recruiting.
“When should I use crowdsourcing and when should I use a managed crowd?” Find out which is best for you human annotated data needs.
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