Blog

May 12, 2016 · post

May 24 Online Event: Text Summarization

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Please join us May 24 at 1:00 pm EST/10:00 am PST for an online discussion about how recent breakthroughs in deep learning allow us to extract and process meaning from text. This opens up a vast range of applications: summarization, instant translation, semantic search, document clustering, and even speech recognition. We’ll be joined by Agolo, a startup focused on text summarization. You can register here. 

Making language computable has been a goal of computer science research for decades. Historically, it has been a challenge to merely collect and store data. Today, however, it has become so cheap to store data that we often have the opposite problem. Once data has been collected, it’s now a challenge to meaningfully analyze it.

But new neural network techniques are enabling organizations to get a handle on unstructured text. As Agolo CTO Mohamed AlTantawy puts it: 

Our goal is to mimic the human thought process of contextualizing real-time data. For example, if a human sees a news item about the iPhone, she will immediately identify that with her knowledge about Apple: that Tim Cook is the CEO, that the iPhone is their major product, and so on. We aim to mimic this human process algorithmically, creating what we call an AI analyst — a universally available AI that can write simple summaries from any unstructured data set.

At this event, we’ll explore AlTantawy’s comments as we discuss:

  • why summarization is important now
  • why summarization is a gateway to making language computable
  • what the different technical approaches to summarization are and what the tradeoffs exist between them 
  • how companies are using summarization to research large document collections, identify breaking news, or generate compliance reports from meeting minutes
  • where we think this is headed in the near future

This discussion is appropriate for non-technical business leaders who want to understand the strategic value of summarization and engineers/data scientists who want to learn how the technology works. 

We look forward to hosting you on the 24th!

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May 3, 2016 · post

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In-depth guides to specific machine learning capabilities
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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.
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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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