Robohub.org
ep.

269

podcast
 

Artificial Intelligence and Data Analysis in Salesforce Analytics with Amruta Moktali

Salesforce         

by
17 September 2018



share this:



In this interview, Audrow Nash interviews Amruta Moktali, VP of Product Management at Salesforce Analytics, about Salesforce Analytics’ analytic and artificial intelligence software. Moktali discusses the data-pipeline, how data is processed (e.g., noise), and how insights are identified.  She also talks about how dimensions in the data can be controlled for (such as race, gender, or zip-code) to avoid bias and how other dimensions can be selected as actionable so Salesforce can make recommendations—and how they use interpretable methods so that these recommendations can be explained.  Moktali also tells about her professional path, including going from computer engineering and computer science to product management and her experience with intrapreneurship (that is, starting an endeavor within a large organization).

Here is a video demo of Einstein Analytics, and you can watch Moktali’s live in the Einstein Analytics keynote at Dreamforce on Thursday, Sept. 27 at 5pm PT at salesforce.com/live and youtube.com/user/dreamforce.

 

Amruta Moktali
Amruta Moktali, VP of Product Management for Salesforce Analytics, has spent 10+ years immersed in the data and analytics side of popular products. Before Salesforce, she was head of product at Topsy Labs, the social search and analytics company, where her team pinpointed the catalyst tweets that initiated the Arab Spring in Egypt. Topsy was acquired by Apple and is now part of Apple Search technology. Prior to that she worked at Microsoft where she worked on several products including Bing, which she had a hand in shaping with the Powerset team. She earned her bachelor’s degree in computer engineering at Maharaja Sayajirao University in India, and her master’s in computer science at Arizona State University.

 

Links



tags: , , , ,


Audrow Nash is a Software Engineer at Open Robotics and the host of the Sense Think Act Podcast
Audrow Nash is a Software Engineer at Open Robotics and the host of the Sense Think Act Podcast


Subscribe to Robohub newsletter on substack



Related posts :

AI system learns to keep warehouse robot traffic running smoothly

  20 Apr 2026
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.

Robot Talk Episode 152 – Dexterous robot hands, with Rich Walker

  17 Apr 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Rich Walker from Shadow Robot Company about their advanced robotic hands for research and industry.

What I’ve learned from 25 years of automated science, and what the future holds: an interview with Ross King

and   14 Apr 2026
Ross King created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing.

Robot Talk Episode 151 – Robots to study the ocean, with Simona Aracri

  10 Apr 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Simona Aracri from National Research Council of Italy about innovative robot designs for oceanography and environmental monitoring.

Generative AI improves a wireless vision system that sees through obstructions

  08 Apr 2026
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.

Resource-constrained image generation and visual understanding: an interview with Aniket Roy

  07 Apr 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.

Back to school: robots learn from factory workers

  02 Apr 2026
A Czech startup is making factory automation easier by letting workers teach robots new tasks through simple demonstrations instead of complex coding.

Resource-sharing boosts robotic resilience

  31 Mar 2026
When a modular robot shares power, sensing, and communication resources among its individual units, it is significantly more resistant to failure than traditional robotic systems.



Robohub is supported by:


Subscribe to Robohub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence