I am obsessed with automation. My friends know that I am a smart home enthusiast. I enjoy setting up every aspect of my environment for ideal comfort and safety—pressing the start button or saying the command words and watching it go. Smart homes aren’t popular with everyone, perhaps because it seems lazy to rely on an app or a smart speaker to open and shut your blinds and turn off the lights. I love it because I get to be the opposite of a procrastinator (though don’t get me wrong, there is nothing wrong with being a moderate procrastinator). What work can I do now to save myself a lot of rote work in the future?
Automation has been helping people offload repetitive work in agriculture and manufacturing for over a century, and right now it’s transforming business. There are two major areas where automation is key—relieving people of tasks that are boring and repetitive, and eliminating errors. I’ll get into the second one in a later post titled From Automation to Artificial Intelligence.
Crush Boring, Save Money
I learned the value of killing repetitive, mindless engineering tasks when we were in lean startup mode at Katabat. We had big aspirations for our software, but not enough money to hire a ton of skilled programmers to do the work. I deployed Continuous Integration and DevOps (though it wasn’t really called that yet) to automate the build and test cycle and better focus our limited resources. This protected programmer time for the most important, creative tasks.
Fast forward to the present, and we want even more automation. Software development, at its root, is a high-level activity where many of the skills involved in writing code are not yet able to be done well by machines. But if you think strategically, you can find a way to save man-hours by building human skills into the automated process. We vastly increased the work we could do with our limited workforce by automating. We designed our process and the tests, hit a button, and our strategy could carry itself out again and again.
Is it or Isn’t it?
Now, there’s a lot of buzz these days about artificial intelligence (AI) and machine learning. You may wonder whether AI is involved in any of Katabat’s automated processes. The question gets complicated because AI often has a vague and changeable definition. “Augmented intelligence” or “intelligence amplification” may be better terms for the functions that matter most to businesses. Processes, analysis, and data management are outsourced to the computer, while humans retain control at key strategic points. These technologies are more and more mature, and with ever-increasing processing power, are totally changing the business landscape (see: Big Data).
Engineering-driven automation (such as Continuous Integration and Test Automation) and AI-driven automation (such as a customer service chatbot powered by Natural Language Processing) are clearly very different. Most true AI, like the computer vision technology (such as facial recognition) I worked on in my doctoral research, is still in an early adopter stage (I can’t wait to get my iPhone X to try FaceID). And no matter how promising a technology is, if it is not thoroughly proven and tested in every possible scenario, it is not the right choice for managing the sensitive data and relationships at major global financial institutions. Banks trust us to be one step ahead of them, introducing new tools just as they mature, but not fifty steps ahead!
In my next posts, I will look in more detail at the promise and problems of artificial intelligence and share Katabat’s plan for introducing proven machine-learning approaches in our upcoming products. Click here and here to read more.
Never hesitate to contact me at email@example.com with your thoughts about automation, AI, or the state of the industry. I love to learn what other technology leaders are thinking about!
Ye cofounded Katabat in 2006 and continues to enjoy creating technology solutions to solve business problems. Ye’s deep experience in artificial intelligence, banking and internet technologies have and continue to shape Katabat’s product development and evolution. Prior to Katabat, Ye worked for Bridgeforce and Ensuredmail, Inc. Ye received a BS and Master’s degree in Electrical and Electronics Engineering from Sichuan University. He also has his Master’s and Ph.D. in Computer Science from the University of Delaware. Ye is an avid technologist and has published multiple conference and journal articles in the fields of computer vision, pattern recognition, and artificial intelligence.
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