Foundation Models

Snorkel AI, Google Cloud and Vertex AI partner to help organizations transform data into AI-powered systems faster than ever.

Amanpreet Singh, Lead Researcher at Hugging Face gave a presentation entitled Towards Unified Foundation Models for Vision and Language Alignment a Snorkel AI’s Foundation Model Summit in January.

Twelve speakers shared their insights into the present and future of foundation models January event; see what they had to say.

Ali Arsanjani, director of cloud partner engineering at Google Cloud, presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit.

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. What are they and how will they impact your business? Find out in our guide.

Combining foundation model outputs with weak supervision yields faster model development and requires fewer ground truth labels.

Snorkel AI CEO and Co-Founder Alex Ratner’s introduction to data-centric AI from the 2022 Future of Data-Centric AI virtual conference.

Brown professor Stephen Bach tells Snorkel CEO Alex Ratner about his research into improving foundation models like GPT-3 with curated data.

Understanding and quantifying people’s opinions has become increasingly important to businesses, but the way people can express multiple thoughts in the same sentence has frustrated practitioners’ efforts to extract those opinions cleanly—a problem we can solve through aspect-based sentiment analysis (ABSA).

Meta senior applied research manager Anoop Sinha and Snorkel AI co-founder Braden Hancock discuss mastering speech and search with TWIML host Sam Charrington.

Together, Snorkel AI and Seldon enable enterprises to adopt AI across the business at scale by dramatically accelerating development and deployment and tightening the feedback loop to rapidly respond to data drift or changing business requirements.

Most poll respondents at Snorkel AI’s recent Foundation Model Virtual Summit named questionable accuracy as the biggest barrier preventing them from getting organizational value from Foundation Models.

Snorkel CEO Alex Ratner interviews Mayee Chen about how Liger improves the effectiveness of programmatic labeling through foundation model embeddings.

Snorkel AI co-founder and CEO Alex Ratner recently interviewed several Snorkel researchers about their published academic papers. In this video, Alex talks with Ryan Smith, Senior Applied Scientist at Snorkel, about the work he did on using foundation models to build compact, deployable, and effective models.

Snorkel AI held its Foundation Model Summit Jan 17, bringing together 12 presenters and over 600 attendees at 10 virtual sessions. The event drew registrants from across many sectors, including the tech industry, healthcare, and financial services.

Snorkel AI co-founder and CEO Alex Ratner talks with Ananya Kumar about the work he did on improving the effectiveness of foundation models by using contrastive learning, image augmentations, and labeled subsamples.

While a majority of Natural Language Processing (NLP) models focus on English, the real world requires solutions that work with languages across the globe. This demo shows how effectively users can build cross-language models in Snorkel Flow.

Using Snorkel Flow, Pixability has created a way to build classifiers for massive amounts of YouTube data quickly—that was previously out of reach.

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.

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

AI is generally accepted as necessary for organizations across private and public sectors to build (or maintain) a competitive advantage. However, a major challenge to adopting AI successfully is our ability to build reliable, predictable, and equitable solutions. A critical flaw with traditional approaches to developing AI is the reliance on hand-labeled training datasets and/or “pre-trained” black-box models that are effectively ungovernable and unauditable. In this article, we explore the motivations and challenges for Trustworthy AI that we’ve encountered and discuss how core tenants of Data-Centric AI, including programmatic labeling, help ameliorate them.

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

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