Blog

Jul 27, 2016 · post

Late Summer Reading List

It’s July 27. 89 degrees with high humidity on the sweltering New York City streets. We’re hard at work on our probabilistic programming report and prototype and looking forward to some good reads on the beach. 

Here are our recommendations for the dog days of summer!

Homage to a great physicist, in a textbook: David MacKay, a Cambridge physicist, influenced the machine learning field by fusing together Bayesian methods with artificial neural nets. He passed away in April. Information Theory, Inference and Learning Algorithms is a meaty textbook with lots of good examples comparing inference techniques. 

Machine learning resources for developers and data scientists: Google’s TensorFlow, released late last year, is a popular deep learning library. O’Reilly recently released a Hello, TensorFlow! post to help you start playing around and developing your first graph. Denny Britz also has a great series on using TensorFlow to build domain-specific chatbots. And if you’re into the chatbot craze (like 95% of Fast Forward Labs enterprise clients), check out this overview in The Morning Paper. 

Science fiction that helps us imagine the future: If you’re into science fiction as just-barely-masked political critique, check out Malka Older’s Infomocracy, about the information powerhouses of our age. If you’re into role-playing games and virtual worlds, Empires of Eve narrates the history of how a virtual world dovetailed with real-world events since its inception in 2003. And if you’re into essayistic travel narratives, check out J. M. Ledgard’s Terra Firma Triptych, which takes an expansive and hopeful look at how technology could better serve developing countries. 

Ethics and algorithms: Enterprises adopting machine learning must invest time to make sure it is used fairly and in compliance with regulations. That’s tough, and is getting tougher. The European Union recently passed legislation mandating a “right to explanation” that could curtail adoption of deep learning tools, as in financial services in the United States. We anticipate upcoming discussions on what explanation means in the machine learning domain and ever more studies like this one by Bolukbasi (et al) on quantifying and reducing stereotypes in word embeddings. 

Corporate social and policy issues affiliated with technology: There’s a lot of talk about machine learning’s impact on employment. Andy Stern, a former labor organizer, suggests the solution of universal basic income to help prepare for a world without work (we don’t believe work will be the province of AI anytime soon, but do think we should think about these issues critically). As regards gender equality in the workplace, we like Iris Bohnet’s What Works as a framework for addressing the problem at its roots. 

Podcast department: If you prefer listening to reading, we like Data Skeptic (for more technical stuff) and Fashion Is Your Business (for interesting innovations in fashion and retail). 

Have a great August! 

- Kathryn

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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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