It is from the 1960s that industrial automation began to be talked about, as a way of using technology to train a machine to do tasks by itself, simulating what would be a process and human intelligence.
Basically, the challenge of these pioneers and researchers was how to create artificial intelligence. That is, how to simulate the functioning of the human mind through programming, so that a machine equipped with this type of intelligence can analyze behaviors and make predictions.
Therefore, the beginning of the use in the industrial and technological field of Artificial Intelligence is what has led us to this point, since current robotics has exponentially increased its applications and the effectiveness of its way of working.
But this is only the basis, and in order to get more out of Artificial Intelligence, it is necessary to have the incorporation of other technologies that expand the possibilities of technological systems such as artificial vision.
Artificial Intelligence: The Beginning of Industry 4.0
The beginning of what is considered the Fourth Industrial Revolution has a lot to do with the incorporation of Artificial Intelligence into the industrial world and its use to be able to access massive data processing efficiently (Big Data).
For now, the data is the one that shows the success of integrating these advanced technologies, since for all types of data science companies automated with Artificial Intelligence, the benefits have increased, at the same time that they have achieved a significant reduction in costs.
And this is because Artificial Intelligence allows data collection, processing and analysis not comparable to human capacity, accompanied by a response based on predictions, patterns and behavior models that reduce margins of error.
But as with all disruptive technology, its evolution and optimization depends on other innovations that allow it to move towards more complex processes and decision-making in which more variables can be included.
It is here that Machine Learning and Deep Learning appear and their potential for the development of automated learning, which expands the possibilities of robotics and artificial vision.
Machine Learning is based on the use of Artificial Intelligence development and it refers to the ability of machines to learn on their own.
We are talking about an automated system capable of self-programming based on what it is learning and the experience itgets from combining data and information processing.It is a type of technology that is based on the creation of complex algorithms that allow a system by itself to collect data, analyze it, learn from it and suggest a logical and justified answer.
In general, when referring to Machine Learning, we talk about the development of techniques and algorithms that make it possible for machines to acquire learning and not limit themselves to the data that humans introduce into their programming.
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As for Deep Learning, it is a subset within Machine Learning that focuses on the development of techniques to facilitate unsupervised learning.As its name suggests, it is the way to achieve deep learning using informative models and creating artificial neural networks for the transmission and analysis of data.
In this case, Deep Learning aims to create a system that is like a human brain, so that the machine also learns from its own mistakes and is capable of modifying solutions and decisions, based on the modeling of abstractions.
This process enables a machine to face more complex contexts, where it depends on the interconnection with other systems and where the volume of data forces it to take into account many variables from which it must learn.
With these nuances, it is easier to see the difference between Machine Learning and Deep Learning, since they are referenced within the development and sophistication of Artificial Intelligence.
Machine Learning and Deep Learning in the evolution of machine vision
The development of all these fields -especially Deep Learning- is what allowsrobots and machines that already recognize, analyze and simulate natural language or expression through images.It is already a functionality available to everyone.We have smartphones with facial recognition or we have virtual assistants with whom we communicate through voice.
And now there are more ambitious challenges, such as the development of autonomous driving, based on the fact that these intelligent machines can recognize patterns, learn from them and carry out different actions, where they take into account the reduction of errors and accidents.
If we focus on the possibilities of these technologies in the development of artificial vision, we are talking about the best capabilities of a machine to analyze images and apply different measurements and variables to identify a product, analyze its condition and decide what to do with it. And all this without stopping the production chain or producing bottlenecks that reduce productivity.
Artificial vision acts like a human eye and brain, but with greater precision, from the application of geometric, physical and statistical calculations in a matter of seconds. We are talking that a robot equipped with artificial vision can have absolute control over everything that happens in a production chain, anticipating problems and executing the necessary actions to avoid it.
This translates into more productive, more efficient industries that make better use of their resources and obtain greater benefits, thanks to the precision of the data handled by intelligent machines.
Challenges and challenges for machine vision
There are many industries that have artificial vision to carry out their quality processes or pick and place tasks due to how accessible this technology is. But the limitations are when industries need non-predefined software with unlimited functions, which is provided by a machine that learns and trains itself.
Currently, the biggest challenges to continue expanding the possibilities of Machine Learning and Deep Learning in already automated systems such as artificial vision, is to be able to have computers with greater processing capacity, to really act at the level of a neural network and to be able to face the millions of calculations required to handle such a volume of data.
Expanding and democratizing the possibilities of Deep Learning and Artificial Intelligence means that the agility at which industries will operate and any automated activity will achieve a highly relevant degree of precision. Production processes will be more efficient, sustainable and will have a better impact on society and on the obstacles it faces in the era of machine learning.