What is Machine Learning?

What is Machine Learning, Simply?

Machine learning, or rather the idea machines can learn to ‘do’ without an explicit set of instructions (programming), has been the basis of many movies where humans end up getting the short end of the deal. But is machine learning truly that dire?

Unlikely. Machine learning, which is a subcategory of artificial intelligence, is simply a way for machines to imitate intelligent human behavior. It’s a type of data analysis that allows programs to learn via experience in order to complete complex tasks, much like humans problem-solve. This type of learning typically breaks down into two specific types: deep learning and reinforcement learning. But what’s the difference?

Deep Learning

Deep learning is essentially what you see in any young child as they start to understand that while chickens are birds, not all large birds are chickens. It is based upon the ability to classify both the common features (in this case: feathers, beaks, wings, etc) as well as the uncommon features that separate each grouping from each other (sound, size, feather pattern, beak length). This kind of hierarchical feature learning stacks multiple layers of learning nodes as observed data from one layer produces new outputs that are then fed to a higher level.

In deep learning, the machine begins with raw data that must then be sorted into relevant and irrelevant subsets. The machine, exposed to more data, improves over time. This is similiar to how a baby learns.

Reinforcement Learning

Meanwhile, reinforcement learning relies more on trying out slight variations of a problem. As results occur (favorable and unfavorable) data sets change until the best outcome emerges. This is reminiscent of “The Good Place” as Michael tries to create a better version of his neighborhood.

Reinforcement learning uses a closed-loop algorithm where each action receives feedback in a trial-in-error process until the best action is determined.

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How To Manage Equipment Obsolescence

No one wants expensive unplanned downtime. Here are key steps to avoid it when dealing with obsolete technology.

close up of a toothed gear system.  Such systems may be affected by equipment obsolescence.
No one wants downtime because of equipment obsolescence. Keep those gears turning.

Unplanned downtime due to equipment obsolescence.  

It’s a dirty subject no one wants to talk about. And for good reason.  A 2016 study by the Aberdeen Group put downtime costs across industries at a whopping $260,000 per hour, up 60% from 2014 data.  A 2014 Gartner study was even worse: placing the average cost at $336,000/hour.   One lost eight-hour shift could mean a $2.08 million to $2.68 million loss. 

Regardless of who’s right about the costs, eliminating downtime and disruption is a high priority for most businesses.   Yet more than two-thirds of companies don’t have a full understanding of when their equipment should be maintained, upgraded, or replaced.  

While we’re excited about the future of  Industry 4.0 and IIoT, where every machine can self-analyze its needs and tell you what to do before there’s a problem, we also understand most factories and industrial locations aren’t there yet. They likely won’t be for decades as they continue to operate with older systems in place. 

But old doesn’t mean unusable.  Here’s why. 

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AX Control Scholarship Winners Announced

Close up of glasses on an open book.  The AX Control Scholarship rewards hard work.
Thank you to everyone who applied for the AX Control Scholarship.

AX Control, Inc is pleased to announce that Zaven Hamazaspyan is the winner of the 2020 AX Control Inc Academic Scholarship. This year’s scholarship is in the amount of $1,000. We chose the winner based upon the strength of a short essay. This focused on a personal account of an ambition he/she had to establish their own business, or of a small business that had impacted their life.

Due to the quality of the essays received, an additional $500 was awarded to a second student. The recipient of this award is Evan Rene MacLaughlin.

Thank you to all the students that participated in this year’s scholarship process. We received essays from around the country, including from students from Ivy League schools like Harvard, Yale, and Princeton. Applicants also pulled from other competitive schools like NYU, Stanford, Duke, and Johns Hopkins.

Please check back soon for information on our 2021 Scholarship application.