Mar 28, 2018 · newsletter

Unemployment vs. Augmentation

In the ongoing debates around whether or not robots are going to take our jobs, listening to those who have a real stake in the technology is critical, and often offers important insights for how we build new technologies, as well as how we talk about what we build. Take, for example, this blog post by Judy Gichoya about the Radiology Society of North America’s annual meeting last December, which provides a useful window into the concerns radiologists have about the ways automation will affect their profession, and how those concerns could be taken into account when building capabilities that impact their domain of expertise. Radiologists are justified in seeing their jobs as directly within the sights of automation, even if the tech is not there yet. (Kaggle’s Data Science Bowl 2017 offered a $1 million prize for submissions that could best detect lung cancer from CT scans, a problem that is squarely within the existing job function of radiologists around the world.) But although the risk is real, the idea that any algorithm will replace all radiologists overnight can be easily dismissed. There is more to radiology than just reading film, questions of liability and responsibility are far from being solved, and image classification tools are not nearly accurate or generalizable enough yet for wide scale deployment.

That said, let’s look at how radiologists are talking about the impact of AI on their occupations for some important insights. The first of these is that zero-sum framing doesn’t help anyone. The blog author points to a Stanford press release that celebrates their researchers’ algorithm that “can diagnose pneumonia better than radiologists.” The adversarial framing of the release is problematic, and not just because the Stanford algorithm had to be tested against radiologist-assigned labels (for a more thorough discussion of the testing and performance comparison methodology, see the original paper). Posing the issue as a man vs. machine problem forecloses a more worthwhile discussion on how image classification can be used to aid radiologists in their work, making them more efficient, accurate, and valuable in the roles they currently hold. This is a point developers could be making from the outset, rather than defaulting to a triumphalist tone of voice, stating that the latest algorithm has defeated humans once and for all.

How medical professionals model the AI development workflow. Image Credit:

Collaborations between domain experts and developers can be built into the very earliest stages of a project. Radiologists know their workflows, what hard problems pertain to their field, where advances are needed, and where automation can help (rather than displace) their expertise. The same principle applies to other roles and industries as well, and this conversation among radiologists illustrates how people in general can respond to advances in AI that affect their domains. Radiologists are already planning how to be useful to the development of AI technology; developers can certainly welcome them to the conversation, and we hope the same will happen across the board.

For a thorough examination of how radiologists work, not just in using their expertise to read films, but to ally with techs and other diagnostic specialists, care for patients, and function within the hospital setting, “CT Suite: The Work of Diagnosis in the Age of Noninvasive Cutting” by Barry F. Saunders is a fascinating read that addresses the history of radiology, the epistemology of diagnosis, and the work of medicine in ways that offer valuable insights for anyone building medical technology.

Read more

Apr 10, 2018 · post
Mar 28, 2018 · newsletter

Latest posts

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. more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck. more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers. more
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. more
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. more
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. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

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.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.