In this presentation, find out how Dr. Greg Benson brought ML into the SnapLogic platform and how to combine the strengths of industry practices and academic methodologies to achieve success with ML.

1. ©2018 SnapLogic, Inc. All Rights Reserved.
The AI Mindset
Bridging Industry and Academic Perspectives
Greg Benson
Professor of Computer Science, University of San Francisco
Chief Scientist, SnapLogic Inc.

2. Every organization of every
size and every industry will
need to employ AI/ML
technology going forward

3. Artificial Intelligence (AI)
is aspirational
Machine Learning (ML)
is practical/applied AI

4. 1992 early academic work
4
Learning continuous-space navigation heuristics in real-time
Gregory D. Benson and Armand Prieditis
Appears In:
From Animals to Animats: Second
International Conference on Simulation of
Adaptive Behavior
- Honolulu, Hawaii, December 1992
Figure 1: An Example Navigation Problem

5. 2010 first industry ML project - predictive field linking
5
• First work with real data
• Lots of data analysis
• Model building
• Experimentation
• Production

6. 2017 Iris Integration Assistant
6

7. Observations
7
Academia Industry
Theory, Scientific Rigor Real Data, Practical Problems
Relatively Flexible Deadlines Fixed Deadlines
Poor Code Quality Slightly Better Than Poor Code Quality

8. Academic/industry collaboration in practice
8
• Professors like to work with students
• Professors like to work on interesting problems
• Professors *need* to publish
• Hire university faculty part-time so they continue teaching
• How it works: USF and SnapLogic

9. What is machine learning?
9
• Machine learning is software
• Machine learning is driven by data
• Machine learning is continuous
• Machine learning can be fickle and flawed

10. Challenges with ML/AI projects
10
• Finding talent
• Access to data
• Hard to quantify the benefits and impact on revenue
• You need more than just a data scientist

11. Getting ML/AI talent
11
• Work with professors and students
• Fund online ML/AI classes for current engineers
• Consider having your data scientists and engineers
teach at local Universities
• Make your data available for research

12. How to think about ML/AI projects
12
• Enable access to data (and not just once)
• Understand the experimental nature of ML
development
• Allocate deployment resources up front
• Develop a plan to determine the success of an ML
deployment
• Consider self-service data science

13. Figure 1: An Example Navigation Problem
Define your goal
13

14. ©2018 SnapLogic, Inc. All Rights Reserved.
Thank you
gbenson@snaplogic.com
benson@usfca.edu