Using AI to Create a World without Waste

Every day we make a choice — to recycle, to compost, or to simply discard an item. But regardless of what decision the consumer makes, corporations are one of the biggest contributors to environmental damage. 

“Recycling and composting and all that stuff. It's great, but it's not where the real problem is, the real problem is the way we make things and move things around. It's the factories, and supply chains. If you want to reduce waste in the world, that's where you target.”

Reducing waste is an ambitious challenge to tackle, but it's one Steve Pratt, Founder and CEO of Noodle.ai, is facing head-on. The strategy: use artificial intelligence and machine learning to help manufacturers and supply chain distributors limit the amount of waste they produce everyday. On this episode of IT Visionaries, Steve explains just how much waste is produced yearly by industrial operations  and the monetary ramifications that waste brings. He also explains the way his team is using A.I. to help manufacturers predict hiccups with their machines, and why it's time for users to overcome their fear of A.I. Enjoy this episode.
 

Main Takeaways:

• It’s Not a Hardware Problem: The biggest problem facing industrial machines remains the software that runs them. In order to cut down on waste, the software must be consistently updated and optimized based on the working conditions.
• Data Library, Data Lake, Data Everywhere: There’s been an explosion of data, and yet corporations still can’t figure out how to put it all to good use.One way to use data is by extracting it from industrial machines, which can be in operation for decades, and then using it to design A.I. solutions based on variables a machine needs to run on.
• You Have to Test Your Models: When you are trying to build an effective modeling solution, you should consistently test your models to understand if your predictive models are working as they should. Make sure you take your time and test over and over again before implementing. A vast majority of A.I. solutions fail because they are not properly tested.

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