Robotics, Machine Learning, and the Internet of Things
The term Internet of Things (IoT) refers to a large network of physical objects, or “things,” that are embedded with sensors, software, or other technology that can exchange data or otherwise communicate with other devices or systems over the Internet. A pervasive example of IoT in our current culture can be seen in wearables, such as smart watches and other fitness trackers. These devices continuously sense movement, communicate the data to another device, and provide fitness insights based on the information it tracks.
Robotics is similar to IoT because it also interacts with sensors, processes data, and responds to requests. However, the main difference is IoT works within a virtual environment while the robotics field works within a physical environment through robots that usually participate in production activities.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software to become more accurate at predicting certain outcomes without being specifically programmed to do so. Machine learning algorithms use statistics to find patterns in large amounts of data. It is used in filtering search engine results, filtering email spam, or websites making personalized recommendations.
Robotics, machine learning, and IoT are all evolving and working together. Since IoT generates massive amounts of data from millions of devices and machine learning is powered by data and generates insight from it, combining the two has the possibility to deliver insights that have otherwise been hidden in data. Machine learning for IoT could be used to project trends, detect irregularities, and more. The development of the Internet of Robotic Things (IoRT) could lead to autonomous networks capable of carrying out tasks in the physical world. The combination of IoT, machine learning, and robotics offers great potential to carry out complex physical tasks over smart networks.
What possibilities do you see when combining these technologies? Tune in to Prefecture’s next podcast for a more in-depth discussion.
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