Unsupervised Learning With Minimalism


A demonstration of Unsupervised AI might be when a robot can think and act responsibly within a given environment, akin to what humans would typically do. In order for machines to replicate human intelligence they require two critical elements, as do humans; time and data. A human exhibits intelligence by first collecting/absorbing data over a period of time. With the integration of data and time markers, any machine that can replicate cognitive processes may exhibit intelligence.

An actual unsupervised learning machine requires negligible human interference. In the same way that a human baby expands its intelligence through observation and guidance, an autonomous machine may evolve simply by observation. Guidance expedites the learning process; however, it might have further implications. You could say that if you aim to prepare a machine for unsupervised learning, you simply need to install an application that will faithfully collect data from an array of integrated sensors. These data will be employed for learning and decision making, and subsequently, these decisions are coordinated back to various motor components without any human interference in the routine. This translates to the negation of tech companies or multiple engineers that might otherwise be necessary to make the machine capable.


Making Robots Think

As per Industrial Robot statistics, there are already 1.3 million industrial robots and another report estimates domestic household robots will rise to 31 million between 2016 and 2019. Another report predicts a boom in service robots in 2019. Most of these robots are built to carry out a specific task which can either be hardwired to do a specific task or could come with a framework to process intelligence within a given set to carry out any one of the capable tasks like a driverless car.

Imagine these machines to learn and behave just like humans as they collect more data to evolve. Industrial robots can learn to avoid accidents, Household robots can learn new tasks and Service robots can know and analyze a lot more about their customers. They can also learn languages like we do and create a more productive and agile environment.

This requires the robot to learn, think and perform just like we do, which would require a deep learning framework that can learn and construct topics with base parameters collected from the sensor devices. They could easily measure, predict and prevent based on past learning and can regress the data further to achieve best case scenarios.

ResponsibleMachines are working on an AGI(Artificial General Intelligence) framework, capable of supervised and unsupervised learning, enabling machines to learn and behave like humans. The data design of the framework is modeled around the HTM theory and is capable of auto-classification and real-time pattern extraction.

Click here to view a detailed article on how robots can emulate humans.