Modern computers are able to recognize a face in a photo, just as the latest generation of smartphones autonomously inserts the objects they are photographing in a specific category. Today, all this is normal, but until a few years ago it was unthinkable. To what do we owe all this? The answer is deep learning, a subcategory of artificial intelligence and, more specifically, of machine learning. If you want to know how to apply the features of deep learning to your business, contact us at Humable. We will be sure to find the right solution to improve the performance of your company, reducing management costs and implementation times.
How does deep learning work?
What is the meaning of deep learning? It can literally be translated as “in-depth learning” and, in fact, indicates the learning of data not provided by human beings, but rather learned through sophisticated statistical calculation algorithms. These algorithms have a primary purpose: to understand the functioning of the human brain, so as to develop something akin to autonomous thought and to imitate human behavior.
Considerable progress has been made in the hardware sector, optimizing the computer’s performance and “training” it to manage and process a very large volume of data. The training time has been significantly reduced, thanks to the introduction of GPUs in deep learning, that is new units that participate in the collection and processing of data.
deep learning not only collects data, but does it in a hierarchical fashion, so that it can be processed and used on different levels. In this way, the machines are able to classify both incoming and outgoing data, using only that necessary to solve a problem. Machines act like humans, learning complex functionalities, but in a much shorter time frame.
What are the artificial neural networks of deep learning?
To understand what deep learning is, we need to analyze the concept of artificial neural networks, which in a certain sense imitate the human brain, which instead works through biological neural networks.
The human nervous system is made up of over 100,000 interconnected neurons that classify and select the most relevant data, to arrive at a conclusion and make a decision. Human neurons provide deep learning with the data it needs to rely on in the use of artificial neural networks.
To clarify this, let’s look at an example. A baby of a few months old, who sees his mother smiling, smiles in turn. Why does he do this? Nobody taught him, but over time he learned to recognize his mother’s smile and therefore tends to reproduce it without real logic. In this process, therefore, the experience takes over that guides learning and offers the brain the necessary data to understand. Similarly, as this child grows up, he will learn to distinguish the tone of his mother’s voice, which can have various inflections: angry, commanding, kind, etc. Based on the tone of the voice, the child acts accordingly.
Exactly the same thing happens in deep learning processes. The programmer only has the task of entering the data into the machine, which re-processes this, so as to gain experience over time and provide the right answers, even when new data is found. The machine is not programmed, but trained so that it can expand its knowledge and provide continuously improving performance over time.
Examples of the use of deep learning
The machine learning of computers in recent years has made great strides, so much so that it has found application in various sectors and is getting closer to human performance, such as facial, linguistic and voice recognition.
deep learning is applied in driverless cars; in drones for the delivery of packages or for assistance in emergency situations (transport of blood and food in flooded or earthquake areas); in facial recognition; in image recognition to detect tumors and diseases on X-rays; in simultaneous translation; in the classification of objects within a photograph; in the automatic generation of a text, etc.
Among the many fields of application of deep learning, there is recognition and speech and linguistic synthesis for chatbots and service robots. We at Humable are able to implement this service, adapting it to the needs of each company, especially in the customer care and customer support processes.
What does the chatbot do? It provides intelligent answers to customers accurately and in real time. It is perfectly capable of extracting information and processing it to give the most appropriate answer, based on the customer’s question. If it is unable to solve a particularly complex problem, it will turn the question over to an operator and learn from the answers provided.
At Humable, we are able to integrate the chatbot with any digital channel: website, app, Telegram or Messenger. Would you like to know more?
Just contact us to find an efficient solution to your problems.