NLP

Sirisha Rella, Technical Product Marketing Manager at Nvidia, recently gave a Lightning Talk presentation on “demystifying” speech AI at Snorkel AI’s Future of Data-Centric AI virtual conference.

Snorkel AI will hold a free Foundation Model Virtual Summit on Tuesday, January 17 where speakers from across the technology industry, including some from Google and Stanford University, will discuss the enterprise use of Foundation Models.

Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.

See what’s in our latest Snorkel Flow release and how we’re accelerating data-centric AI development further.

More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.

A central innovation team at a top US bank wanted to modernize its AI development and data annotation processes in order to create a custom natural language processing (NLP) model that could extract important financial information from 10-Ks. Manually reviewing these documents was taking up valuable time that could be better spent assisting customers. The team used Snorkel Flow’s data-centric AI development process and programmatic labeling to train a customized NLP model that could accurately extract information on interest rate swaps.

Georgetown University’s CSET is building next-generation NLP applications using Snorkel Flow to classify complex research documents. Snorkel Flow drastically reduced labeling, model training, and iteration time and better equipped CSET’s data science team to collaborate closely with analysts to gather, process, and interpret data at scale. 

The recent debut of ChatGPT astounded the public with the power and speed of foundation models, but their enterprise use remains hampered by adaptation and deployment challenges. In the past year, Snorkel AI has researched several ways to overcome those challenges. 

Snorkel AI is delighted to announce a partnership with Aimpoint Digital, a premier analytics firm specializing in AI application development that builds, operationalizes, and scales data science solutions for biopharma, manufacturing, retail, and other major industries. Aimpoint Digital leads the industry in solving complex challenges and exploiting value-generating opportunities for organizations of all sizes through data. The company helps clients

The Snorkel AI team will present five research papers advancing weak supervision and programmatic labeling at the NeurIPS 2022 conference that started this week.

Snorkel AI is excited to build on our partnership with Microsoft Azure to help enterprises and government agencies solve their most impactful problems and unlock value from their data using AI. Learn how Azure customers can easily deploy Snorkel Flow on their Azure cloud infrastructure to accelerate AI application development with data-centric workflows and programmatic labeling.

Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the AI space by storm, going viral—even beyond technical practitioners—thanks to incredible capabilities around text generation, image synthesis, and more. However, enterprises face fundamental barriers to using these foundation models on real,

We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise use cases. The need is clear: despite undeniable excitement about

Create a data-centric AI application using Snorkel Flow to save your analysts time of manual labeling and information extraction related to environmental, social, and governance (ESG) factors from earnings call transcripts. Rapidly and accurately extract all existing and new factors from the transcripts to make the right investment decision.

To meet the requirements of unexpected regulatory changes brought on by the pandemic, a top-10 US bank needed to urgently adapt its underperforming model-centric artificial intelligence and machine learning development approach to a data-centric one. The team used Snorkel Flow to automatically classify thousands of loan documents and extract critical clauses in just 24 hours, saving loan managers thousands of hours of manual document review.

Schlumberger is the world’s leading provider of technology and services for the energy industry, operating in over 120 countries. The company provides well maintenance and analytics services to the world’s biggest oil companies, and it believes that large-scale data analysis and artificial intelligence/machine learning will help them remain a leader in the market. One way they’ve been able to achieve this is by building their own AI application using Snorkel Flow to automatically extract geological entities and critical field data across a variety of document structures and report types they receive from their customers.

This blog post introduces variants of Precision, Recall, and F1 metrics called Precision Gain, Recall Gain, and F1 Gain. The gain variants have desirable properties such as meaningful linear interpolation of PR curves and a universal baseline across tasks. This post explains what these benefits mean for you, how the gain metrics are calculated and outline some examples for intuitive comparison. 

Snorkel AI just hosted the second day of The Future of Data-Centric AI conference 2022. Across 40+ sessions, 50+ Data scientists, ML engineers, and AI leaders came together to share insights, best practices, and research on adopting data-centric approaches with thousands of attendees from all around the world. Aarti Bagul, a Snorkel AI ML Solutions Engineer and one of the

Snorkel AI just hosted the first day of The Future of Data-Centric AI conference 2022. This conference brings together data scientists, ML engineers, and AI leaders to share insights, best practices, and research on how to evolve the ML lifecycle from model-centric to data-centric approaches. This conference takes place over two days with 40+ sessions, 50+ speakers, and thousands of

Building NLP techniques to understand 10-Ks is time-consuming, costly, and challenging. In this post, Machine Learning Engineer, Aarti Bagul discusses three information extraction case studies on how banks around the world are building highly accurate NLP applications using Snorkel Flow’s AI platform. From retail banking to hedge fund investing, NLP is used across the financial industry. By processing and extracting

Programmatic labeling moves a classic technique from interesting to high-impact So much of real-world AI development entails working with text data that’s messy — in fact, 80%+ of enterprise data is unstructured. And while state-of-the-art models get a lot of the glory, creating the training data that conveys what your model needs to learn is more often the biggest determiner of AI

AI systems are well-suited to tasks involving recognizing and predicting data patterns. Supervised classification systems categorize unseen data into a finite set of discrete classes by learning from millions of hand-labeled labeled sample points. These classifiers are powerful business tools – they automate document sorting, customer sentiment analysis, sales performance, and other distinct business problems. However, they also require an

What is data annotation? Data annotation refers to the process of categorizing and labeling data for training datasets. This process plays a critical role in preparing data for machine learning models, as high-quality training data enables more accurate predictions and insights. In order for a training dataset to be usable, it must be categorized appropriately and annotated for a specific

Labeling functions are fundamental building blocks of programmatic labeling that encode diverse sources of weak labeling signals to produce high-quality labeled data at scale. Let’s start with the core motivation for labeling functions: over time, every major commercial organization and government agency builds various valuable, often bespoke knowledge resources. These resources include employee expertise, wikis and ontologies, business logic, and

Research recap: Ontology-driven weak supervision for clinical entity classification in electronic health records (EHRs)  In this post, I have summarized the research published in this academic paper, Ontology-driven weak supervision for clinical entity classification in electronic health records by Jason Fries et al. This paper was published in Nature Communications in 2021.Problem statement Electronic health records (EHR) contain a rich

Highlighting the best practices for building and deploying AI models for financial document processing applications AI has massive potential in the financial industry. Building AI models to automate information extraction, fraud detection, and compliance monitoring can provide efficient and faster responses and support repurposing domain experts’ labor to more meaningful tasks. Developing AI models is not just about having models

The following post is based on a talk discussing the benefits of programmatic labeling for trustworthy AI, which was presented as part of the Trustworthy AI: A Practical Roadmap for Government event that took place this past April, with Snorkel AI Co-founder and Head of Technology, Braden Hancock. If you would like to watch Braden’s presentation, we have included it

If you were ever amazed at how Google accurately finds the answer to your question just by a few keywords, you’ve witnessed the power of named entity recognition (NER). By quickly and accurately identifying different entities in a sea of unstructured articles, like names of people, places, and organizations, the search engine can figure out each article’s main topics and

The future of data-centric AI talk series In this talk, Assistant Professor of Biomedical Data Science at Stanford University, James Zou, discusses the work he and his team have been doing from a data-centric perspective to trustworthy and interpretable AI. If you would like to watch James’ presentation, we have included it below, or you can find the entire event

Gregory Ihrie is the Chief Technology Officer for the FBI, responsible for technology, innovation, and strategy. He also leads the FBI’s efforts in advancing the bureau’s management, policy, and governance of AI systems. Ihrie chairs the FBI’s Scientific Working Group on Artificial Intelligence, as well as the Department of Justice’s AI Committee of Interest. He is one of three officers