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At the end of next month, I’ll be hanging up my project manager hat and embarking on a new journey. Over the past year, I’ve been balancing my role here at Oak City Labs with graduate school as I’m pursuing my Master’s of Arts degree in Teaching from Meredith College. Though I’ve spent the better part of the past decade working in the technology industry, I felt a shift in my goals and interests and, with the amazing support of the Oak City Labs leadership team, I decided to make a career change.

Why tell you this?

Because in some ways, I’m not really leaving the technology industry at all. I’ll just be applying my skills in a different way to a different set of “clients” (read: elementary school students).

In my time spent in graduate school and in field placement positions this past year, it has become increasingly clear to me that there is more of a need for globally-minded, technologically-equipped educators than ever before. The reality is that educators need to be preparing students for jobs that don’t even exist yet. Yes, you read that correctly. According to the World Economic Forum, 65% students entering elementary school now (aka my future “clients” if you will) will hold jobs that don’t even exist yet. And that data is two years old. The numbers have certainly increased since then.

I think about some of my recent blog posts on artificial intelligence, machine learning, computer vision and machine vision. As cutting edge as these technologies are, odds are they will have significantly evolved by the time current primary and younger secondary school students graduate high school in 8-10 years. Therefore, instead of preparing students for specific jobs, we are charged with preparing students with skill sets that will grow with them as this world also grows.

Figuratively, that preparation is a multi-layered, interdisciplinary approach to learning beginning with the earliest grades through high school graduation. It looks different for every student and every teacher. Practically that preparation begins with integrating meaningful technology in the classroom, expanding student learning through social studies and science, as well as enhancing student understanding through the arts.  

The hope is through all of our efforts, we’ll prepare students not for the jobs that artificial intelligence will certainly replace, but for new jobs that work alongside artificial intelligence. While machine vision may eliminate the need for a factory worker to inspect products, machine vision will certainly create the need for software engineers to manage the inspection system. And desirable software engineers will need to possess specific skills in technology, along with soft skills like critical thinking/problem solving, collaboration, communication and creativity/innovation.

So I leave this role, company and industry with a lot of change ahead, but I’m hopeful that my efforts will foster students that are better prepared for those jobs that don’t even exist yet. And maybe even some future employees of Oak City Labs.

PS – Did you hear? We’re currently on the hunt for a project manager and software developer. Check out our Careers page for more information and job details.

Machine learning (ML). Artificial intelligence (AI). Internet of Things (IoT). It seems these days there are so many complex terms flying about. In this blog, we’ll break down the basics and share how you’re experiencing these technologies in ways you may not have even realized.

Artificial Intelligence

We can’t talk about machine learning without first discussing artificial intelligence. Long before ML and IoT were commonplace, the concept of AI existed. The term was coined by John McCarthy decades ago in 1955 describing it as, “making a machine behave in ways that would be called intelligent if a human were so behaving.”

Simply put, AI is when machines handle tasks in an intelligent manner in the same ways humans would. Did you know you have AI all around you? Apple’s Siri, Amazon’s Alexa, Google’s personal assistant, and Tesla cars are all examples of AI at work.

Machine Learning

Machine learning is a product of artificial intelligence. Where AI is the ability of machines to handle tasks in an intelligent manner, ML takes it one step further and says that machines should not only handle complex tasks the same intelligent way humans would, but they should also have the ability to learn how to handle the complex tasks themselves. This concept is also decades old, dating back to 1959 when Arthur Samuel coined this term defining ML as, “the ability to learn without being explicitly programmed.”

Do you know that saying ‘all squares are rectangles, but not all rectangles are squares’? Well, the same is similarly true here: all ML is AI, but not all AI is ML.

Interested in the technical aspects of ML, including how neural networks are used? Our software engineer Taylor shared his thoughts and experiences on the blog before.

So how are you experiencing machine learning in your everyday life? Amazon and Netflix recommendations are great examples. As the two learn your buying or watching habits, they make recommendations of what you may be interested in next – and most times, they’re spot on! Or have you ever noticed that as you use your iPhone more and more for texting that it begins to pick up on your common spellings, acronyms and dialects? That’s machine learning.

My name is Ashlee. It’s spelled a little differently than most Ashleys, but my parents spelled it that way when I was born and I haven’t found it on keychains ever since, so what can I do? Apple didn’t program the iPhone to know that when I start typing Ashlee to not autocorrect it to the more popular Ashley, but after several times of me typing and then undoing the autocorrect to change the spelling back to Ashlee, my phone learned that when I type Ashlee, I mean Ashlee not Ashley. That’s machine learning.

And what about Internet of Things?

Artificial intelligence and/or machine learning can converge in the physical world with Internet of Things. IoT is the category of physical objects – like lightbulbs, thermostats, door locks, plugs, etc. – that are connected to the world via the Internet. When those IoT devices also employ ML, like when a Nest thermostat learns your temperature preferences and adjusts the temperature inside your house for you without your action, the two worlds are colliding.

At Oak City Labs, our mission is help businesses and organizations solve daily problems with technology. Utilizing AI and/or ML are excellent ways to accomplish that task. Do you have a problem that you need help solving? If so, let us know! We’d love to chat.

Read more about Computer Vision & Machine Vision here.