At a recent Microsoft Developers Build Conference in San Francisco, one presenter discussed how robots are being designed to do human work through the use of machine learning (ML). Take, for instance, lettuce cutting. A new robot from Blueriver Technology, known as LettucBot, uses ML algorithms to cut lettuce precisely. Blueriver’s motto is “smart machines for digital agriculture.”
Robots Handling Vastly More Variables
It’s not that robots are smarter than humans, it’s that they have more capability of handling significantly more variables than humans. Humans handle 10 to 15 variables at a time while ML algorithms handle thousands. What’s more, ML algorithms can be designed to slice and dice information to identify hierarchical layers of information, create outlines, and synthesize this information to make decisions in a split-second or less.
Cobots Will Give Way to Just Robots
A current trend in robotics known as cobots, where humans and robots work together, is likely to fade into history as ML, AI, and sensor and motion capture camera technology gets better and faster. The ultimate goal is use machine learning to develop cognitive computing systems that will integrate AI and machine learning to perform increasingly humanlike tasks including thinking, planning, and decision-making.
Use of Machine Learning in a Variety of Industries
Already in the investment world, hedge funds including Two Sigma and Renaissance Technologies are using ML and AI to design and manage investment portfolios.
The following video is a speech about ML by Tom Simonite of Google. According to the video description:
Google has deployed practical A.I. throughout its products for the last decade — from Translate, to the Google app, to Photos, to Inbox. The teams continue to make fundamental breakthroughs in machine learning, publishing promising new results at an accelerating pace. Now TensorFlow and Cloud Machine Learning make it even easier for researchers and developers around the world to collaborate. So as we work together to drive machine learning forward, what are the most exciting possibilities? What are the top challenges? And what’s on the horizon? Join Google’s machine learning leads in a discussion with veteran technology editor Tom Simonite as we explore the promise of machine learning.