AI IRL Podcast Episode 37: How A.I. Can Increase Manufacturing Efficiency and Profits
When most people hear “A.I.” they think of robots or talking cars or even Skynet becoming self-aware. But, as I’ve been saying over and over again, the applications to A.I. are much broader than that.
1. They are making manufacturers more efficient
Mass-manufacturing is all about efficiency. Over time, they become very lean. Manufacturers are used to having a tightly controlled financial situation. So, they want to know: how can I run my machines harder and longer without affecting my production quality?
This works for complex manufacturing like the biotechnology or semiconductor industries, or relatively simple manufacturing like mining or construction. All of them require their production to run at peak efficiency.
So, if a construction company is finding that their cement pump isn’t getting them as much cement as they thought it should, this is an inefficiency that needs to be dealt with.
Efficiency means more money. And by applying A.I. and machine learning, these mass-manufacturers are finding ways to increase their efficiency.
For instance, an electronics company that is producing at scale could be making thousands of the same parts from dozens of factories all over the world. Applying machine learning to their production process would increase efficiency, maintain consistent quality, and therefore increase productivity. In a mass-manufacturing environment, this could mean a profit increase in the tens of millions of dollars per year!
“And so in our world, applying A.I. means that we are able to make businesses more efficient, which can make them more profitable and more competitive, and can make them capable of offering a higher value product to give to consumers at potentially the same price.”
2. They are helping companies apply machine learning
Companies know that the future of manufacturing is smarter, more efficient automation. However, building a machine learning infrastructure is time consuming and requires a different skill set than many domain experts possess.
Falkonry is stepping in to help companies set up machine learning processes that work for them. One of the ways they do this is through automated feature learning. So, by automating the feature learning process, Falkonry helps the company get better systems to help their production process.
The company itself doesn’t have time to build out the feature datasets or come up with a comprehensive list of what features are needed. They need to run the company!
So, by automating those processes, Falkonry helps companies apply machine learning in ways that they couldn’t have on their own.
3. They are making A.I. accessible to domain experts
Lastly, one of the coolest things Nikunj is doing is trying to get high-end machine learning into the hands of domain experts. Rather than A.I. being kept in a secret vault deep in a research university, Nikunj and the people at Falkonry are passionate about getting the latest in AI technology in the hands of the people on the ground in industries and fields where A.I. can have the biggest impact.
“The ideal scenario is where machine learning becomes accessible to those who have the knowledge of the domain. It’s a quest, I think, to make new technology accessible to people who have been in the field so that they can truly leverage that technology and create tremendous impact.”
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