After the publication of my article SparkCognition: Let machines address security threats, a representative of another supplier, Sight Machine, reached out to get my attention.
Getting the analyst's attention
Sight Machine's PR rep keyed in on the phrase "using a combination of cloud computing, big data, machine learning and predictive analytics to predict, find, and resolve security threats" found in the post on SparkCognition and suggested that speaking with representatives of Sight Machine would be interesting. After all, he pointed out, it "uses a combination of cloud computing, big data, machine learning, and visual analytics to reduce manufacturing defects." Although manufacturing automation is way outside of my normal area of research, I agreed to speak with Jon Sobel, CEO and one of the founders of SightMachine and former general counsel of Tesla Motors, and Nate Oostendorp, CTO and fellow founder whose history includes being one of the original members of the Slashdot team.
What got my interest was the fact that Sight Machine started out by helping companies use machine vision, machine learning, and an early version of the internet of things to help get manufacturing companies to create better products faster. The fact that another one of the founders of Slashdot, Robin "Roblimo" Miller, is a long-time friend helped as well.
Manufacturing is just beginning to use machine learning and analytics
Sight Machine developed sophisticated machine vision solutions and used them to help manufacturing companies improve levels of quality. Their machine vision tools made it possible for companies to automate the process of examining parts as they come out of a machine to see if the part meets the required specifications. During its history of helping companies optimize their manufacturing processes, the company learned that most manufacturing companies are not using the latest computing, networking, or virtualization technology inside of their manufacturing plants.
Each numerically controlled machine is treated like a silo. Data that it collects is stored only for a short time and discarded quickly. Smart Machine is doing its best to convince these companies that capturing that data, integrating it, and using analytical tools to learn from that data will help them create better products and reduce costs. Sight Machine soon saw that its infrastructure software could be used to gather data from all of the numerically controlled manufacturing machines, put it into a form usable for business intelligence and analytics, and burst through the silos the manufacturing companies has created.
The vision of the future
What really attracted my interest was Sight Machine's view of the future. They are proponents of manufacturers building out an "Internet of Things" inside of their plants that reaches out to their sales force and then to the end customer. Wouldn't it be useful, for example, if an intelligent automobile could "phone home" when something started to fail, automatically retrieve software updates or, at the very least, help the repair pros by pre-ordering parts and having them sent to the dealer.
Wouldn't it be useful if those same automobiles could contribute real-time road date to the supplier to help beef up product designs?
I found myself thinking of how Sight Machine's infrastructure could be used to help manufacturers of many types of products, in essence, use their machine-oriented social media to help improve their products.