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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Executive Briefing: Best practices for human in the loop—The business case for active learning

Paco Nathan (derwen.ai)
3:30pm–4:10pm Thursday, 09/13/2018
Data-driven business management, Strata Business Summit
Location: 1E 14 Level: Non-technical
Average rating: ***..
(3.00, 1 rating)

What you'll learn

  • Gain an introduction to active learning and human in the loop for AI, including brief case studies, vendor landscape, emerging themes, and open source projects available
  • Understand the business case for how and when to use active learning
  • Learn how to blend human intelligence and judgement with algorithms and data for a best-of-breed approach to AI in the enterprise

Description

Consider two deep-rooted notions in Silicon Valley: software is eating the world and more data beats better algorithms. Both have merit, but they don’t give the full picture. Deep learning (DL) works well when you have large, carefully labeled datasets to use for training, but not every organization has data assets like those of Google, Amazon, and Apple.

To leverage machine learning while cultivating those assets needed for DL—in other words, table stakes for AI—one excellent approach is active learning, a variant of machine learning that incorporates human-in-the-loop computing. Active learning blends human intelligence and judgement with algorithms and data for a best-of-breed approach to AI in the enterprise. Many enterprise organizations are at a stage appropriate for this kind of work.

One interesting benefit is that active learning focuses input from human experts. On the one hand, it leverages the human intelligence that’s already in the system: customer support, sales teams, professional services, etc. To paraphrase Amazon’s Werner Vogels, “There’s no compression algorithm for experience.” On the other hand, active learning provides systematic ways to explore and exploit the uncertainty within your data, identifying likely opportunities for profit.

Paco Nathan offers an introduction to active learning and human in the loop for AI, making the business case for how and when to use it. You’ll leave with new ideas that you can immediately use to get your team talking, thinking, and taking action.

Topics include:

  • Case studies (with proven ROI)
  • Best practices
  • The vendor landscape
  • Emerging themes and issues
  • Available open source projects
Photo of Paco Nathan

Paco Nathan

derwen.ai

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 Media and director of community evangelism at Databricks and Apache Spark. Paco is the co-chair 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.