The Problem
Machine Learning is currently powered by armies of humans. Improving the accuracy of machine learning models requires increasing the amount of ingested labeled data and/or re-labeling existing data. Deep Learning models in particular perform best with meaningful training datasets that contain millions to billions of examples for complex machine learning applications, and it takes months and massive amounts of manpower to get them labeled. These human-powered labelers are:
Expensive
Time consuming
Prone to
human error
Inconsistent
Not able to handle streaming data
Without labeled data, machines cannot be trained to learn new tasks.
Meet Jaxon
Jaxon is the first “AI for training AI,” labeling raw text with meaning and bridging the gap to supervised learning.
With self-adjusting pipelines, Jaxon adapts to each organization’s nuanced data and domain-specific terminology. Training sets are created using existing data, as well as from new text streaming in from online and internal sources. Jaxon’s Studio allows users to curate meta model(s), tune pipelines, and ensemble labelers to optimize training sets.
The Benefits
Jaxon offers a dramatically faster solution to human labelers (minutes vs months) that is cost efficient, consistent, more accurate, and compatible with streaming data analytics.
Saves time
Saves money
Increases accuracy
Works with streaming data
Supports Continuous Learning
Humans cannot compete with the consistency and speed of Jaxon.
Use Cases
Jaxon labels become features for training supervised machine learning models and making real-time predictions.
Jaxon’s output feeds directly into:
Conversational platforms, e.g. Google Dialogflow and Amazon Lex
Make better predictions
Enhance experiences for each user
Understand the meaning of text and the contextual relevance
Power search engines (e.g. Elasticsearch) with extracted entities
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