Natural Language Processing (NLP) for short refers to the manipulation of speech and text by software.
Ground truth data is the objective, provable data used to train, validate and test models. It is directly related to the task that needs to be achieved. AI cannot set the objectives. It is the job of humans.
With businesses looking for more and more data to enable machine learning, many are turning to synthetic data to fill in the gaps.
There are many different types of learning that you may encounter as a practitioner in the field of machine learning — here are some of the most important.
Linguistic diversity in AI means more access, inclusion, and use cases for everyone who benefits from machine learning.
Any given machine learning workflow defines which phases are implemented, and how, during a ML project.
Across all industries, conversational AI for customer service and success is used to cut costs and increase client and prospect engagement.
Medical image annotation saves time and lives. Learn more about its use cases.
Conversational AI is the synthetic language and brainpower that makes human interactions with machines more effective and natural.
Audio annotation services are a subset of data annotation that focuses on tagging audio data.