#20 – Active Machine Learning with Dr. Jennifer Prendki

In Algo Podcast Episode 20, Dr. Jennifer Prendki and Amjad Hussain discuss Active Machine Learning.

Skip, recap, or review with timestamps:

0:00 Introduction
3:29 Jennifer’s AI Journey
5:15 Collecting Good Data
6:04 Particle Accelerator & Physics Research
8:00 Physics Standard Models
10:18 Are Standard Models Still Useful?
13:49 Data Outliers
15:31 Next Physics Breakthrough
19:12 Aliens, Dark Matter & Energy
21:12 Difference between Antimatter & Dark Matter
24:24 Decay Curve for Electrons & Positrons
26:48 Discovering New Particles
28:32 Relationship Between Quantum Nature & Space Time
30:30 What is Artificial Intelligence & Machine Learning?
32:58 Why can people perform better than Supervised Machine Learning?
37:24 Machine Teaching
38:51 How to improve Supervised Learning
43:02 Focusing on variance instead of volume
43:45 ROI for organizations
45:40 How to build diverse data sets
48:25 Understandability in models & data
50:09 Active Machine Learning
53:33 Understanding where models are struggling
56:09 How to learn active learning approach
58:44 Using Active Learning to Prioritize Data
1:04:08 Role of synthetic data
1:09:41 How can people interact with trained models?
1:12:20 Meta Machine Learning
1:13:09 Advice for people learning ML

About the host

portrait of Amjad Hussain, one of the author for Algo podcast and the CEO of Algo


Combining human centered AI with deep domain expertise, Algo’s analytics enriched supply chain intelligence platform helps suppliers and retailers plan, collaborate, simulate and execute a more efficient supply chain.

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