Blog

Mar 28, 2018 · newsletter

New creative possibilities with machine learning

Machine learning techniques are able to organize large amounts of unstructured data. Combined with dimensionality reduction techniques like t-SNE, this capability opens up new ways for us to interact with creative material including sounds, words, and ideas. In this section we highlight three of our favorite recent experiments.

A screenshot of The Infinite Drum Machine. Showing the sample map with certain sounds like “Bag Plastic” highlighted and a drum sequencer at the bottom.

The Infinite Drum Machine is a Google Creative Lab experiment by Manny Tan and Kyle McDonald. It uses machine learning to cluster a large number of samples by similarity of sound. The user then selects clips, which can range from “gravel scoop tin cup” to “casino poker chip,” from a sound map visualization created using t-SNE. The samples feed into a sequencer to create an uncanny drum machine.

Conceptually, the project works because it plays off the long history of hip-hop and electronic musicians using samples from surprising sources (one of Grant’s favorite, kind of gross, examples is Matmos’ “A Chance to Cut is a Chance to Cure”). Sometimes machine learning systems give disappointing results because the system lacks context. In this case, the lack of context is a virtue. It frees the system up to make connections between sounds that humans, with their knowledge of each sound’s source, might never make. It reminds us of first learning to draw, where you have to let go of your idea of what an apple looks like and draw the apple exactly as it appears in front of you.

Samples map well because each sound has a quantifiable (wave) form. Anything involving words gets more complicated. Do you compare on similarity of phoneme, grapheme, or meaning?

A screenshot of an example from “Voyages in Sentence Space.” It shows this example:“I went looking for adventure. I went out on a mission. I shouted awkwardly. I stared incredulously. I feel desperate. I never returned. I never returned.

In Voyages in Sentence Space, Robin Sloan uses machine learning to explore the possibility space between sentences, which he calls “sentence gradients” (we love that metaphor). Instead of showing the whole map, it focuses on a specific journey from one sentence to another. As for how it does comparisons, Robin tweeted about one illustrative example where, according to the model, ‘“thousand” is more like “three” than it is like “hundred” because of the “th."’

A screenshot of Encartopedia. On the left is the Wikipedia article on Euclid. On the right is a visualization of the articles on Wikipedia and a line showing the user’s journey through other articles to get to the Euclid article.

Sepand Ansari’s Encartopedia, which he made while working with us here at CFFL, looks at visualizing Wikipedia articles as an idea map, and plotting the user’s journey through that space. It suggests how new capabilities can help us reflect on our thought process, by showing us how we move through an idea space.

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Mar 28, 2018 · newsletter
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Latest posts

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Oct 20, 2022 · newsletter

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CFFL September Newsletter
September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance.
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by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
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Reports

In-depth guides to specific machine learning capabilities
FF24

Text Style Transfer

The NLP task of text style transfer (TST) aims to automatically control the style attributes of a piece of text while preserving the content, which is an important consideration for making NLP more user-centric. In this report, we explore text style transfer through an applied use case — neutralizing subjectivity bias in free text. Along the way, we describe our sequence-to-sequence modeling approach leveraging HuggingFace Transformers, and present a set of custom, reference-free evaluation metrics for quantifying model performance. Finally, we conclude with a discussion of ethics centered around our prototype: Exploring Intelligent Writing Assistance.
Read the report  →
FF22

Inferring Concept Drift Without Labeled Data

Concept drift occurs when the statistical properties of a target domain change overtime causing model performance to degrade. Drift detection is generally achieved by monitoring a performance metric of interest and triggering a retraining pipeline when that metric falls below some designated threshold. However, this approach assumes ample labeled data is available at prediction time - an unrealistic constraint for many production systems. In this report, we explore various approaches for dealing with concept drift when labeled data is not readily accessible.
Read the report  →
FF19

Session-based Recommender Systems

Being able to recommend an item of interest to a user (based on their past preferences) is a highly relevant problem in practice. A key trend over the past few years has been session-based recommendation algorithms that provide recommendations solely based on a user’s interactions in an ongoing session, and which do not require the existence of user profiles or their entire historical preferences. This report explores a simple, yet powerful, NLP-based approach (word2vec) to recommend a next item to a user. While NLP-based approaches are generally employed for linguistic tasks, here we exploit them to learn the structure induced by a user’s behavior or an item’s nature.
Read the report  →
FF18

Few-Shot Text Classification

Text classification can be used for sentiment analysis, topic assignment, document identification, article recommendation, and more. While dozens of techniques now exist for this fundamental task, many of them require massive amounts of labeled data in order to be useful. Collecting annotations for your use case is typically one of the most costly parts of any machine learning application. In this report, we explore how latent text embeddings can be used with few (or even zero) training examples and provide insights into best practices for implementing this method.
Read the report  →

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

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