Blog

Jul 16, 2015 · post

Fast Forward Labs at the Science Fair

Last night was the New York Times’ Open Source Science Fair. It kicked off with a keynote speech by our own Hilary Mason.

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Photo by Chrys Wu: http://t.co/m7G7G3g1ih

After a dinner meet-and-greet session, the attendees and exhibitors got down to business, showing off their open source software projects, science fair style.

Fast Forward Labs was an exhibitor. Micha Gorelick impressed the crowd with our realtime stream analysis prototype, CliqueStream.

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CliqueStream visualizes the landscape of conversation on Reddit and Twitter, in realtime. This type and scale of analysis would generally be handled by a high-powered cluster and would take upwards of ten minutes to complete. But by using probabilistic algorithms, we are able to analyze the streaming data in realtime on a laptop.

The probabilistic modules underlying CliqueStream are used to distill stream data by counting it and later selectively “forgetting” it; this is the key to speedy analysis in small memory. These appropriately named modules are open source.

CliqueStream was just one among many great open source projects at the Fair. Two of our favorites were the New York Public Library Labs’ book cover generator and MIT’s App Inventor software.

  • The New York Public Library Labs’ 10PRINT book cover generation algorithm creates cover art for digital books in its collection, many of which are public domain titles that never had cover art.
  • MIT’s App Inventor software is a teaching tool that allows kids to develop Android apps.

The Fast Forward Labs team had a great time learning about these and the other presenters’ work. Thanks to New York Times Labs for a great event!

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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.
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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.
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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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