Teaching a machine to learn typically involves three primary components: data, features, and algorithms.
Machine learning requires large amounts of diverse data to find patterns and “learn.” There are two main ways to collect that data: manually or automatically. Manually collected data contains far fewer errors but takes more time to collect and therefore, is more costly, as it is labor-intensive. The automatic approach, on the other hand, is less costly to conduct, since the system is gathering everything it can on its own and is essentially “hoping for the best.” Either way, the more diverse the data, the better the results will be.
Also known as parameters or variables, features are the building blocks of data. They provide the algorithms with the characteristics or properties needed to perform a given calculation. These characteristics are usually numerical, but they can also be graphical. Selecting the features that most distinctly characterize or represent the data is vital and often time consuming. The quality of the features selected has a major impact on the quality of the insights gained through machine learning. This process is difficult for a human to do by hand. In addition, the main source of error often results from these features not being describable or understandable by humans Feature-selection processes, or feature engineering, can improve the quality of one’s data. This is often a difficult process, but if performed well, the resulting data will contain the features essential to solving the problems. This, in turn, can lead to building the possible data models.
Machine learning relies upon different types of algorithms to learn. These include supervised methods such as classification and regression, as well as unsupervised methods like clustering or pattern search.
RE2’s intelligent robotic systems use multi-modal 2D and 3D imaging sensors with RE2 proprietary algorithms that can perceive the world in both indoor and outdoor environments.