Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA
Paco Nathan

Paco Nathan
Evil Mad Scientist, derwen.ai

Website | @pacoid

Paco Nathan is known as a “player/coach” with core expertise in data science, natural language processing, machine learning, and cloud computing. He has 35+ years of experience in the tech industry, at companies ranging from Bell Labs to early-stage startups. His recent roles include director of the Learning Group at O’Reilly and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.

Sessions

4:15pm-5:00pm Wednesday, September 5, 2018
Open source decentralized data markets for training AI in areas of large shared risk
Blockchain and AI Summit
 Location: Imperial B
Paco Nathan (derwen.ai)
Paco Nathan examines decentralized data markets. With components based on blockchain technologies—smart contracts, token-curated registries, DApps, voting mechanisms, etc.—decentralized data markets allow multiple parties to curate ML training datasets in ways that are transparent, auditable, and secure and allow equitable payouts that take social values into account. Read more.
4:50pm-5:30pm Friday, September 7, 2018
Executive Briefing: Best practices for human in the loop—The business case for active learning
AI Business Summit
 Location: Continental 4
Secondary topics:  AI in the Enterprise
Paco Nathan (derwen.ai)
Average rating: **...
(2.00, 1 rating)
Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data. Read more.