Applied AI

Snorkel AI to host Foundation Model Virtual Summit, registration now open

January 5, 2023
3 min read

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.

Registration is now open for the summit, which will begin at 9 AM Pacific Time with an optional in-person portion at Snorkel HQ in Redwood City, CA, to follow from 4 PM to 6 PM.

From GPT-3 to DALL-E 2 and Stable Diffusion, foundation models are driving the rapid advancement of AI, enabling exciting new possibilities for creativity and exploration while pushing the boundaries of what’s possible.

However, using these foundation models in real-world, high-value scenarios can be challenging due to their lack of adaptability to complex, domain-specific tasks as well as their potential cost and governance constraints.

The summit’s presentations will cover best practices for using and deploying Foundation Models for enterprise AI, common challenges faced when adopting foundation models (and how to overcome them), the Foundation Models available today and how they can be applied to ML tasks, and the future of foundation models.

Current list of talks:

  • Alex Ratner, CEO and co-founder at Snorkel AI will deliver the opening keynote address.
  • Ali Arsanjani, director cloud partner engineering at Google, will address the challenges and ethics of businesses adopting dynamic learning maps and large language models.
  • Amanpreet Singh, research team lead at Hugging Face, will focus on unified foundational models for vision and language alignment.
  • Ananya Kumar, ML researcher at Stanford University, will give a tutorial on foundation models and fine-tuning.
  • Braden Hancock, Co-founder and head of research at Snorkel AI, will demonstrate how to transfer knowledge from foundation models into deployable models.
  • Carlo Giovine and David Harvey, Partner and Expert at McKinsey QuantumBlack, respectively, will trace trends in enterprise machine learning and the potential impact of Foundation Models
  • Jay Alammar, director and engineering fellow at Cohere, will talk to the subject of when generative AI is not enough.
  • Jimmy Lin, NLP product lead at Sambanova Systems, will describe a practical approach to delivering enterprise value with foundation models.
  • Joe Penna, Head of Entertainment Technology at Stability AI will give a talk entitled “The Ethical Implications of Building A Real-Life Skynet”
  • Simran Arora, ML researcher at Stanford University, will discuss how Ask Me Anything-style questions can bolster the effectiveness of Foundation Models.

Most talks will be presented live with a Q&A session to immediately follow.

The in-person meetup from 4-6 PM at Snorkel HQ will offer an opportunity to network with fellow Bay Area data scientists and meet Snorkel’s co-founders and members of the team.

The Foundation Model Virtual Summit follows up on Snorkel’s Future of Data-centric AI event in August 2022, which drew more than 5000 registrants and featured more than 50 sessions and 70 speakers.

Share this article

Recommended articles

View all articles
os-world-reading-group
OSWorld 2.0: Why Long-Horizon Computer-Use Agents Still Fail Four Out of Five Tasks
Mengqi Yuan (XLANG Lab, University of Hong Kong) presents OSWorld 2.0, a benchmark of 108 long-horizon, real-world computer-use workflows where even frontier AI agents complete only 20.6% of tasks outright after 300+ steps each.
September 3, 2026
Snorkel Team
Image
Fable 5.1 on Frontier Coding Tasks: Efficient Successes, Distinct Failure Modes
We evaluated Fable 5.1 on a series of frontier coding tasks from our proprietary Terminal-Bench+ dataset and compared the results against Opus 5. Fable remained competitive across most categories and was materially more efficient on successful runs, while its gap was concentrated in a small set of terminal-heavy and build/dependency tasks. Because category sizes are small and uneven, we treat
September 1, 2026
Ankit Aich
,
Jonathan Schlosser
Image
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
The speed of new frontier model releases keeps accelerating. Meanwhile benchmarks struggle to keep up and saturate quickly, often being left in the dust. Most benchmarks are static datasets with no active maintenance, causing them to lose value fast. Some benchmarks are looking to change this by becoming Continuous Benchmarks. Terminal-Bench is one of the most widely reported benchmarks on
August 28, 2026
Justin Bauer
Image

Join our newsletter

For expert advice, the latest research, and exclusive events.
By submitting this form, I acknowledge I will receive email updates from Snorkel AI, and I agree to the Terms of Use and acknowledge that my information will be used in accordance with the Privacy Policy.