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©2018 SnapLogic, Inc. All Rights Reserved.
The AI Mindset
Bridging Industry and Academic Perspectives
Greg Benson
Professo...
Every organization of every
size and every industry will
need to employ AI/ML
technology going forward
Artificial Intelligence (AI)
is aspirational
Machine Learning (ML)
is practical/applied AI
1992 early academic work
4
Learning continuous-space navigation heuristics in real-time
Gregory D. Benson and Armand Pried...
2010 first industry ML project - predictive field linking
5
• First work with real data
• Lots of data analysis
• Model bu...
2017 Iris Integration Assistant
6
Observations
7
Academia Industry
Theory, Scientific Rigor Real Data, Practical Problems
Relatively Flexible Deadlines Fixe...
Academic/industry collaboration in practice
8
• Professors like to work with students
• Professors like to work on interes...
What is machine learning?
9
• Machine learning is software
• Machine learning is driven by data
• Machine learning is cont...
Challenges with ML/AI projects
10
• Finding talent
• Access to data
• Hard to quantify the benefits and impact on revenue
...
Getting ML/AI talent
11
• Work with professors and students
• Fund online ML/AI classes for current engineers
• Consider h...
How to think about ML/AI projects
12
• Enable access to data (and not just once)
• Understand the experimental nature of M...
Figure 1: An Example Navigation Problem
Define your goal
13
©2018 SnapLogic, Inc. All Rights Reserved.
Thank you
gbenson@snaplogic.com
benson@usfca.edu
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The AI Mindset: Bridging Industry and Academic Perspectives

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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.

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The AI Mindset: Bridging Industry and Academic Perspectives

  1. 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. 2. Every organization of every size and every industry will need to employ AI/ML technology going forward
  3. 3. Artificial Intelligence (AI) is aspirational Machine Learning (ML) is practical/applied AI
  4. 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. 5. 2010 first industry ML project - predictive field linking 5 • First work with real data • Lots of data analysis • Model building • Experimentation • Production
  6. 6. 2017 Iris Integration Assistant 6
  7. 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. 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. 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. 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. 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. 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. 13. Figure 1: An Example Navigation Problem Define your goal 13
  14. 14. ©2018 SnapLogic, Inc. All Rights Reserved. Thank you gbenson@snaplogic.com benson@usfca.edu

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