AI Block: Your custom machine learning model based on the training data you have trained it with.
API: Application Programming Interface - a method for software programs to communicate with each other using code. Learn more about APIs.
Classification: Sorting data points into different buckets. Read more about the subject here.
Confidence: Much of machine learning involves estimating the performance of a machine learning algorithm on unseen data. Calculating the confidence is a way of quantifying the uncertainty of such a prediction.
Extraction: A machine learning task where the AI pulls out specific pieces of information from larger bodies of text.
Flow: We have a visual workflow builder that lets you connect different applications, AI Blocks, and determine the right actions.
Generation: A machine learning task where the AI generates text, such as creating a draft email response or writing a report.
HITL:Human in the Loop - adding a human helping hand into the training and feedback of the model, to increase accuracy over time.
Integrations: We have developed direct connections with various 3rd-party applications that allow you to move data and actions between them in a seamless fashion. We are adding new ones on a continuous basis and you can find the complete overview here.
Label: Also known as a tag, category, target, or class. It is what the model will provide to you for unseen data.
Natural Language: A way to communicate with AI systems using plain, everyday language, much like giving instructions to another person or talking conversationally.
No-code: The ability to create AI models, or set up workflows, without writing any code
Performance: Simply put, how well the AI Block does based on the information it has.
Prediction: What the AI Block believes to be the right label for an unseen data point.
Summarization: A machine learning task where the AI summarizes the main points from larger bodies of text.
Supervised learning: A type of machine learning where the AI learns from labeled training data, and this learned knowledge is used to predict the outcomes of unseen data.
Trigger: Every Flow requires a start signal. This can be a new file, an email attachment, or any other event that you determine to be the starting point of the process.
Workflow: A full series of actions or tasks that process and move data between different applications or services, guided by rules and conditions.
Zero-shot: Making predictions on new, unseen data without the model requiring any explicit examples or training on that specific task beforehand
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