Blog

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: Becominghuman.ai

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.

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Latest posts

Nov 15, 2020 · post

Representation Learning 101 for Software Engineers

by Victor Dibia · Figure 1: Overview of representation learning methods. TLDR; Good representations of data (e.g., text, images) are critical for solving many tasks (e.g., search or recommendations). Deep representation learning yields state of the art results when used to create these representations. In this article, we review methods for representation learning and walk through an example using pretrained models. Introduction Deep Neural Networks (DNNs) have become a particularly useful tool in building intelligent systems that simplify cognitive tasks for users.
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Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
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Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
qa.fastforwardlabs.com
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
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
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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