It can be said – and helps to understand them – that the concepts “Deep Learning” and “Machine Learning” are under the umbrella of Artificial Intelligence. One is within the other and Artificial Intelligence is the whole. That means, the Deep Learning sustains the Machine Learning, which allows machines to think as people do (Artificial Intelligence).

Exposed thus, it is appreciated that they are not the same. But, let’s see more.

 

Differences between Machine Learning and Deep Learning

Machine Learning is the science that allows machines to perform actions without needing to be programmed for it. This is achieved through algorithms (rules that set the necessary step-by-step, instructions, to execute a specific task). Through them data is organized, recognizes patterns and allows machines to “learn” models and generate patterns if you need pre-programming.

Deep Learning is specifically a type of high-level Machine Learning algorithm built on the neural network principle. It mimics the neural network of the human brain.

While Machine Learning began to be developed in the 1980s, is the first way to apply and put into practice the concepts of artificial intelligence, Deep Learning is much more recent science. It appeared in the 21st century, starting in 2010, with a much more evolved and powerful technology that has allowed the consolidation of the Big Data.

Precisely, one of the problems of Machine Learning, in its beginnings, was the limitation of applications due to lack of data and technologies and devices capable of processing them efficiently. It has been with Deep Learning that algorithms capable of supporting and working with Big Data have been developed.

 

Machine Learning and Deep Learning in waste management: the example of Picvisa

We have already commented on the PICVISA blog how Artificial Intelligence transforms the waste management and recycling industry. Being Machine Learning and Deep Learning two of its pillars, it is obvious that they play a fundamental role in this transformation.

Specifically, the main application of Deep Learning algorithms, is in classification tasks, especially image recognition. Always essential tasks in the treatment and selection of waste and, even more, in what has been called Smart Waste Management, the management of smart waste that is an increasingly solid reality of the present.

Being a supplier company specialized in the design, manufacture and supply of optical separation equipment for the classification of materials, recovery and recovery of waste, PICVISA is a clear example of what the application of Machine Learning and Deep Learning contributes to the management of waste.

For example, the robot ECOPICK of PICVISA uses Artificial Intelligence able to recognize and classify a variety of objects on a conveyor belt through algorithmic Machine Learning and Deep Learning. With it, you learn the procedure as you select the different wastes and improve the quality of the items that are recovered.

The company’s foray into these sciences goes beyond a machine since it has in its catalogue BRAIN by PICVISA, the Deep Learning solution designed to complement its other products, the optical separators ECOGLASS and ECOPACK. Its name already conveys the idea of equipping the machines with a “brain” that allows them to choose between 6 different applications, each focused on solving a different problem: recovery of valuables, tray vs. bottle separation, film separation, silicone cartridges, selection of medicines and glass separation by colour.

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