Predictions for 2020 and Beyond
By Lior Elazary
Co-founder and CEO of inVia Robotics
Progress in robotics will be both incremental and exponential in 2020 and into the next decade. We will see incremental improvements in the ability of robots to take on new tasks and add new capabilities. We’ve already seen a steady expansion in the scope of tasks that robots are being relied upon to carry out. In just the last 12 months, robots that had been used only for picking also added replenishing, cycle counting, and putting back returns and mis-picks to their duties.
In 2020, we'll continue to see more responsibilities transferred to robots, including quality check and pack out, and other adjacent areas where companies have been forced to rely on a temporary workforce that’s becoming increasingly scarce. At inVia, we’re predicting a shift toward a reliance on robots for all of the tasks normally assigned to temp workers, which will lead to the creation of more full-time jobs for team members that oversee the automated work.
We will also see incremental improvements in capabilities, such as fine manipulation. One of the most exciting things about new technologies is that as adoption rates increase, adaption rates increase as well. As we continue to see the number of deployments increase, we learn more about how to improve the technology to fit a broader set of use cases, which leads to greater maturity of the products. With robotics—particularly those that incorporate machine learning—that means the mechanisms improve because they get smarter as the data sets expand.
Simultaneously, we will experience exponential improvements in machine learning and artificial intelligence, as well as in component technologies like machine vision and sensors. The same computer science fundamentals will continue to be employed, but we'll learn more from real-world environments so that we can identify opportunities to do things differently—and more efficiently. For example, the mathematical calculations at the core of SLAM (Simultaneous Localization And Mapping)—which is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it—originated back in the 1700s. The full capabilities of that work were not unleashed until relatively recently when we developed enough computing power to allow us to assess all of the probabilities.
In 2020, we expect to be able to broaden our vision to see greater possibilities within machine learning, artificial intelligence and other technologies, which will allow us to identify new ways of delivering automation while opening the flood gates to exponential growth. Personally, I couldn’t be more excited by the prospect of uncovering more problems to solve through the application of automation. The possibilities are truly endless.