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Product posts, company news and explainers on how AI works.

Abstract Jaxon.AI cover image for the post “Towards Unified Machine Learning”

July 9, 2020

Towards Unified Machine Learning

Machine learning tasks are low-level building blocks that can only solve complex, real-world problems after being unified across type/task.

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Toy chicken figurine perched on a brown egg — visual metaphor for chicken-and-egg causality in AI training data

June 19, 2020

The Egg and the Chicken

How can Jaxon train models to produce data that trains better models? Isn’t the first better if it can label data? Which is first, the egg or chicken?

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April 29, 2020

The Puzzle Pitfalls of Building ML Pipelines

Building ML pipelines is like solving a puzzle, but the right tools make the process easier, faster, and with a more stable end result.

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Epoch-versus-error convergence graph showing training loss decreasing from 1.0 to 0.6 across 500 epochs

April 2, 2020

Training Classifiers: “Do You Even Lift?”

After you use Jaxon to label your training set and are ready to embark on training classifiers, it might seem that you’re in the home stretch. However, if you want

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Abstract Jaxon.AI cover image for the post “Machine Learning Amplification: Fast Track to Gold”

March 24, 2020

Machine Learning Amplification: Fast Track to Gold

Get better, faster results using fewer resources with machine learning amplification tools like Jaxon. Hop on the fast track to gold!

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Cost and time savings with human supervision (with a +10% increase to accuracy)

March 13, 2020

Just Enough Human Supervision

We had hubris a couple years ago, thinking you could create accurate machine learning (ML) models completely unsupervised. Turns out, we do need some human supervision—just enough human

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Abstract Jaxon.AI cover image for the post “The ‘Gotchas’ of ML/NLP”

February 24, 2020

The ‘Gotchas’ of ML/NLP

Building machine learning models can be exhilarating, but the process to get to the final model can often be tedious. Inexperienced data science teams may encounter hidden obstacles

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The ideal confusion matrix

January 10, 2020

Confusion Matrices: Dazed and Confused

Confusion matrices are the tried-and-true way to evaluate the metrics for machine learning classifiers. Is updating them really so confusing?

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Abstract Jaxon.AI cover image for the post “Why Use AI to Label Data?”

July 22, 2019

Why Use AI to Label Data?

Long story short, humans are slow, taking at least 10 seconds to label one example. However, a machine can make the same judgement in milliseconds.

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a history of machines

April 10, 2019

A History of NLP Machines

The history of using machines to understand the meaning of natural language started as early as World War II and continues through present day.

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Abstract Jaxon.AI cover image for the post “Natural Language Processing: Progress to Jaxon”

March 15, 2019

Natural Language Processing: Progress to Jaxon

Until recently, natural language processing (NLP) applications have been limited in their ability to understand nuance and context. What's changed?

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