In recent years, many industries have been working with artificial intelligence to improve their production processes. However, this technology has been very complex and costly to implement in different manufacturing industries around the world.
Why? Because it is neither user-friendly nor easy to program. Engineers spend hours writing code to improve the manufacturing and/or production process without seeing real results; and, moreover, for companies, investing in machine vision is very costly, both in terms of resources and hardware.
ARTIFICIAL INTELLIGENCE aspires to function just as the human brain does. Within it, there is deep learning, which is a type of machine learning inspired by the structure and function of the human brain. Rachael.vision has this technology, which is less expensive and much simpler to use, as no specialized knowledge is required. At the same time, hardware development in this field is growing exponentially.
The brain is composed of neurons, which are connected by synapses, and deep learning algorithms are designed to simulate this neural network structure. Deep learning models are composed of artificial neural networks consisting of layers of interconnected nodes, which process and analyze input data to make predictions or classifications. That’s why deep learning is the technology of the future.
In addition, deep learning models use a learning process called backpropagation, which is inspired by the way the brain learns from feedback. During training, the model is presented with examples and feedback on its performance, allowing it to adjust its internal weights and improve its accuracy.
Research in deep learning has also contributed to our understanding of how the brain works. For example, studies have shown that deep learning models trained on visual recognition tasks can produce features similar to those found in the human visual cortex.
While deep learning models are not identical to the human brain, they are inspired by its structure and function; and they have demonstrated impressive performance in a wide range of applications, from image and speech recognition to natural language processing and robotics.
So, we can say that the bridge connecting the human brain and industry is deep learning, but which of its functions can industry use to improve its production processes?
Here we tell you:
- IMAGE AND VIDEO RECOGNITION: deep learning algorithms can be used to analyze and classify images and videos, enabling applications such as facial recognition, object detection and autonomous cars. Rachael.vision allows you to train models to classify objects or to orient robots very easily.
- QUALITY CONTROL: computer vision systems based on deep learning can identify defects and anomalies in real time, ensuring that products meet the highest quality standards. These systems, such as Rachael.vision, can learn from previous data and adapt to new variations in products, making them more reliable and accurate than traditional inspection methods.
- ROBOTICS: Deep learning algorithms can be used to train robots to perform tasks such as grasping objects or navigating through environments, enabling applications such as warehouse automation and industrial robotics. Rachael.vision has the ability to identify, segment and classify objects through in-application training, enabling the integration of deep learning into industrial machines.
We can help you implement all of these functions on machines in your industry, improving efficiency, accuracy and automation, from manufacturing, logistics, production, to healthcare.
Visit us at rachael.vision