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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

Sep 22, 2021 · post

Automatic Summarization from TextRank to Transformers

by Melanie Beck · Automatic summarization is a task in which a machine distills a large amount of data into a subset (the summary) that retains the most relevant and important information from the whole. While traditionally applied to text, automatic summarization can include other formats such as images or audio. In this article we’ll cover the main approaches to automatic text summarization, talk about what makes for a good summary, and introduce Summarize. – a summarization prototype we built that showcases several automatic summarization techniques.
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Sep 21, 2021 · post

Extractive Summarization with Sentence-BERT

by Victor Dibia · In extractive summarization, the task is to identify a subset of text (e.g., sentences) from a document that can then be assembled into a summary. Overall, we can treat extractive summarization as a recommendation problem. That is, given a query, recommend a set of sentences that are relevant. The query here is the document, relevance is a measure of whether a given sentence belongs in the document summary. How we go about obtaining this measure of relevance varies (a common dilemma for any recommendation system).
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Sep 20, 2021 · post

How (and when) to enable early stopping for Gensim's Word2Vec

by Melanie Beck · The Gensim library is a staple of the NLP stack. While it primarily focuses on topic modeling and similarity for documents, it also supports several word embedding algorithms, including what is likely the best-known implementation of Word2Vec. Word embedding models like Word2Vec use unlabeled data to learn vector representations for each token in a corpus. These embeddings can then be used as features in myriad downstream tasks such as classification, clustering, or recommendation systems.
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Jul 7, 2021 · post

Exploring Multi-Objective Hyperparameter Optimization

By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric!
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Jun 9, 2021 ·

Deep Metric Learning for Signature Verification

By Victor and Andrew. TLDR; This post provides an overview of metric learning loss functions (constrastive, triplet, quadruplet, and group loss), and results from applying contrastive and triplet loss to the task of signature verification. A complete list of the posts in this series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification In our previous blog post, we discussed how pretrained models can serve as strong baselines for the task of signature verification.
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May 27, 2021 · post

Pretrained Models as a Strong Baseline for Automatic Signature Verification

By Victor and Andrew. Figure 1. Baseline approach for automatic signature verification using pretrained models TLDR; This post describes how pretrained image classification models can be used as strong baselines for the task of signature verification. The full list of posts in the series is outlined below: Pretrained Models as Baselines for Signature Verification -- Part 1: Deep Learning for Automatic Offline Signature Verification: An Introduction Part 2: Pretrained Models as Baselines for Signature Verification Part 3: Deep Metric Learning for Signature Verification As discussed in our introductory blog post, offline signature verification is a biometric verification task that aims to discriminate between genuine and forged samples of handwritten signatures.
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