Looking Back on 2018's Game-Changing Technology

DSC Logistics CIO Kevin Glynn shares what he thinks were the two biggest innovations of the past year.

By Kevin Glynn

As Chief Information Officer at DSC Logistics, I spend my time working collaboratively with customers to deliver solutions to their technology goals. I focus on IT operations, new system developments, and telecommunications, but in order to be successful, I need to have a grasp on the latest technology news and innovations.

Kevin GlynnIn my eyes, as I look back on the past year, there were two innovations within tech that were game changers. There are a number of fascinating trends that appeared in 2018 and other concerns that continue to be addressed, but combined, these two developments have forced individuals, organizations, and industries to rethink and reimagine the role technology plays in their daily lives. The first game changer is the maturation and commercial viability of artificial intelligence (AI), and the second is the continuing evolution of autonomous vehicles.

Let's start with the rise of AI. Now, to be clear, AI was not created in 2018. The original AI algorithms and the development of deep learning go back to the mid-1980s when the concept was first postulated by PhDs. That being said, it wasn't until about three years ago that AI could go from an idea to actual innovation. Until around 2015, there wasn't the computing horsepower available to run large sets of data, which meant there was no way an AI tool could process the data needed to work effectively.

Today, though, that computing power exists in the cloud, and as a result, AI is booming.

To clarify, AI encompasses three different areas:

  1. Natural language processing (this can be thought of as chatbots like Alexa or Google Home)
  2. Image recognition
  3. Machine learning

All three of these areas matured at a relatively parallel pace in 2018, and as we consider the future of AI in 2019 and beyond, each will likely have a considerable impact on all sorts of industries and professionals. Machines can be taught to read x-rays better than a radiologist. AI can be (and already is being) implemented at call centers around the world, or it could be brought in for forecasting or root cause analysis in any number of fields, from banking to product development.

Perhaps the biggest reason why the rise of AI was such a game changer in 2018, though, was that it became readily accessible. You don't need a PhD in data science to implement it. It's still hard, but no one is shying away from that challenge.

Like AI, autonomous vehicles have been a dream for years, and in 2018, they matured to the point where live testing with the general public could be performed in controlled environments. Think back 10 or 20 years ago. The idea of self-driving cars sounded so far-fetched, but in 2018, they became a reality.

Google, Uber, and others are both in the process of testing driverless vehicles, and I don't think it will be long before we see cars on the road with no one sitting in the driver's seat. And like AI, the impact of these vehicles will be wide-reaching. Sure, they can serve as taxis, but think about the role they could play in a warehouse. Plus, autonomous cars will likely lead to autonomous forklifts or autonomous trucks, and suddenly the warehouse working experience will look far different than it does today.

At DSC Logistics, we've experimented with both AI and autonomous vehicles. We recently tested two robots that were pulling a train of items behind them. It is already in production on a limited basis. We have also tested drones inside warehouses for inventory counting and just started work on autonomous forklifts.  

There still is work to be done to get these machines to see and interact with the world in a way that is safe. There is no margin for error. It's great to say, for example, that the number of times an autonomous vehicle hit something is down from 30,000 to 30, but that 30 still isn't good enough. Zero is the acceptable number.

That is where my other title comes in. In addition to my role at DSC Logistics, I am honored to be the Chair of the Industry Advisory Board for Northwestern University's Master of Science in Information Technology (MSIT). I've had a front-row seat for the latest developments coming out of the MSIT program in the Engineering School and the incredibly talented students who are preparing to be the next generation of tech leaders.

After all, the MSIT degree experience is designed to teach students how to think through the types of problems that arise with game-changer technologies. Twenty years from now, tech will be far beyond where it is today, but fundamental engineering and the understanding and ability to think and manage these problems is what the industry needs. Fortunately, that is exactly what MSIT is teaching.

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