This article has been sponsored by SambaNova Systems and was written, edited, and published in alignment with Emerj’s sponsored content guidelines.

We’ve covered the topic of manufacturing in heavy industry on a number of occasions over the years in our content here at Emerj and it’s a space that has been and continues to be quickly evolving.

In this episode, we explore a range of AI use-cases in the manufacturing space, some more mature than others, and talk about how specific workflows in the manufacturing space might evolve and how those differences might also impact the bottom line. 

SambaNova Systems is a firm that aims to bring innovations in artificial intelligence in advanced research to global enterprises and has raised approximately $1B to bring their hardware and software solutions to market to date. 

We cover two primary topics in this 30-minute interview:

• Understanding the nuance in sensors and their capabilities in heavy manufacturing
• What it looks like to create opportunities for NLP in manufacturing

Listen to the full episode below:

Guest: Everton Paulino, Business Development Executive of Automotive & Manufacturing at SambaNova Systems

Expertise: Manufacturing, Automotive, High-Performance Computing

Brief Recognition: Prior to joining SambaNova Systems in August, Everton held sales positions at Hewlett Packard Enterprise, SGI, and Clover Technologies. He completed a Bachelor of Business Administration in International Finance from Eastern Michigan University. 

Key Insights

• Understanding the importance of vision and audio sensors – when it comes to anomaly detection or predictive maintenance, people often think about vibration and heat sensors on heavy machinery, but audio sensors can be used to determine sound coming from within machines in order to determine if maintenance or human intervention might be required. As with video, some of these sounds might be indistinguishable to the human senses, but it might be possible for machines to pick up on them and alert human operators of problems before they occur.
• Creating opportunities for NLP in manufacturing – Everton speaks about the use-case of NLP inside the vehicle with the example of how manufacturers, in addition to thinking about what’s happening on the shop floor, can also think about their product and how it’s going to interact with customers. In automotive, this could look like users speaking to their car in order to access their Owner’s Manual and ask and receive questions about a specific problem or concern by voice instead of having to manually search through the physical book.