Blog

Aug 29, 2018 · newsletter

Automated Machine Learning: Hype now, reality later?

Previously in our newsletter, we had framed automated machine learning around four notions:

  • Citizen Data Science / ML: Automated machine learning will allow everyone to do data science and ML. It requires no special training or skills.
  • Efficient Data Science / ML: Automated machine learning will supercharge your data scientists and ML engineers by making them more efficient.
  • Learning to Learn: Automated machine learning will automate architecture and optimization algorithm design(architecture search).
  • Transfer Learning: Automated machine learning will allow algorithms to learn new tasks faster by utilizing what they learned from mastering other tasks in the past.

Since then, the term automated machine learning has been strongly linked to Google’s definition of AutoML as a way for neural nets to design neural nets, or - expressed technically - as a way to perform neural architecture search. Google’s messaging asserts that AutoML will make AI work for everyone. Google Cloud’s AutoML beta products now allow one to custom vision, language and translation models with minimum machine learning skills. The product page states that under the hood, this capability is powered by Google’s AutoML and transfer learning. But, as pointed out by fast.ai, transfer learning and neural architecture search are two opposite approaches. Transfer learning assumes that neural net architectures generalize to similar problems (for example, features like corners and lines show up in many different images); neural architecture search assumes that each dataset needs a unique and specialized architecture. In transfer learning, you start with a trained model with an existing architecture and further tune the weights with your data; neural architecture search requires training multiple new architectures along with new weights. In practice, one does not need to use both techniques (yet?)! Transfer learning is currently the predominant approach since neural architecture search is currently computationally expensive. We very much agree with fast.ai’s assessment that not everyone needs to perform neural architecture search, and the ability to perform such a search does not replace machine learning expertise. In fact, blindly using computation power to search for the best architecture seems to lead us further into the abyss of un-interpretable models.

On the flip side, if we go back in time to the pre-GPU era, one could argue that we are at the same place with neural architecture search as we were back then with deep learning. Sprinkle in the notion of Software 2.0, and suddenly the idea of everyone designing neural nets for their particular needs looks like a reasonable trajectory!

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Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series.
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Nov 14, 2022 · post

Implementing CycleGAN

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Oct 20, 2022 · newsletter

CFFL October Newsletter

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Sep 21, 2022 · newsletter

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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Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

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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Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team.
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In-depth guides to specific machine learning capabilities

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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
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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

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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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