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AI and ML Glossary

Explaining the lingo used

The jargon explained:

  • 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.
  • Error rate: We keep some amount of training data for testing and at the end of the training process, we automatically measure how many errors the model makes per 100 predictions. Read more about error handling here.
  • 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. Read more on this topic here.
  • Human review: Our platform comes with the ability to give feedback on predictions that the model is unsure about.
  • 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. Learn more about how we measure performance.
  • 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.
  • Unlabeled data: Any data that hasn't been assigned a category
  • 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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