It is not science fiction, nor is it a thing of the distant future, artificial intelligence is more present than ever, and it is used in more aspects of life than it is possible to imagine. If there is still no clarity about what Artificial Intelligence is, let’s define it. Any system that thinks or acts like a human, or thinks or acts rationally, is artificial intelligence. In this sense, it is the combination of algorithms to create machines that imitate the same capabilities of the human being or that, in other words, can conclude a set of data.
But what is an algorithm? It is a series of instructions that are carried out to solve a problem. The arrival of the digital revolution put this word on everyone’s lips because algorithms and computers can calculate more information than the human brain and at a higher speed. In this sense, it seemed that creativity and emotions were the only complete tasks of humans, or at least they were until the arrival of artificial intelligence.
Artificial intelligence is here to stay and also to transform the world. Today, it is used for facial detection, virtual assistants, chatbots, fraud detection, market patterns, suggesting content based on taste or usage, sales forecasts, predictive analytics, among many others.
The success of artificial intelligence lies in the fact that it is not necessary to make programming for each scenario. Still, it is possible to “teach” things through automatic learning or that the system can learn by itself. It is precisely in this evolution where the focus is placed, since the systems that learn by themselves, through example, allow the anticipation of various problems, thanks to the extraction of behaviour patterns.
Deep-learning vs. machine learning: how are they different?
It is already clear what artificial intelligence is about. Now, it is necessary to delve into two previously anticipated concepts. The ability to teach a system things through continuous learning, also known as machine learning and the system’s power to learn by itself is known as deep learning.
It is a subset of artificial intelligence, through which it is possible to train systems, or machines, to recognize patterns based on data to then make a prediction or suggestion. An example of this is the content suggestions shown by platforms such as Netflix or YouTube based on previous views or emails in the spam or junk mail folder.
It is a subset of machine learning and consists of systems capable of drawing their conclusions by learning independently. Its operation is similar to that of neural networks, so they process information similarly to the human brain.
For example, to teach him to identify a particular object, it will not be necessary to train him on what that object is like; by showing him photos of that object, the system will solve it on its own.
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Differences between machine learning and deep learning
- Both are the pillars that support artificial intelligence.
- Deep learning is an evolution of machine learning. Therefore, it is pretty new; it developed in 2010 with powerful computers and the rise of accessible data.
- In machine learning, algorithms can be supplied with data and learn on their own to make predictions and guide decisions. Whereas deep understanding can support and work with big data and function as a mind of its own through the superposition of non-linear layers of data processing.
Deep learning ushered in a new technological era, and its popularity continues to grow. This branch of artificial intelligence promises to continue revolutionizing different industries, thanks to its extraordinary results that allow accelerated advances in the business.
Deep learning, is it possible to imitate the neural networks of the human brain?
In a very simplified way, the basis of deep learning is to continuously analyze data with a given logical structure, just like the human brain does, but how is this possible? The system works thanks to artificial neural networks, which are nothing more than a structure of algorithms with several layers, connections, and a direction in which the data propagates, passing through each layer with a specific analysis task. These neural networks identify patterns and classify different types of information.
The different layers of the neural networks serve as a filter, going from the most general elements to the most specific, which increases the probability of detecting and generating a correct result.
Each layer of the artificial neural network specializes in a feature and weighs it. For example, in the case of an object, one layer is responsible for analyzing the shape, the color, and so on until all the information is collected and the sum of the weights is made to offer a result.
Deep learning has three types of neural networks.
- Convolutional Neural Networks (CNN) : They are in charge of processing structured matrices, such as images.
- Recurrent Neural Networks (RNN) : They are those that use sequential data or time-series data.
- Generative Adversarial Networks (GAN) : They consist of using two artificial neural networks and opposing them to generate new content or synthetic data that can be passed off as real.
Deep learning and the digital revolution
Deep learning brought a true revolution thanks to the vast possibilities that its use offers. Machines are increasingly complex in their hierarchical model, which has made it possible to simplify processes and achieve many of the things that were previously thought impossible. Some of the advantages that have allowed deep learning to be adopted in so many industries are:
- It is not necessary to define characteristics previously; the system deduces them and adjusts them by itself, which favours the automation of processes.
- Accuracy and reliability in the results it delivers.
- Independence when working, a user is not required to achieve results.
- Ability to interpret data, understand it, and draw conclusions intelligently.
- The more data, the better the system performance.
- The system can evolve and adapt to new problems in the future, thanks to the flexibility of its structure.
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Deep learning and its infinite possibilities
Deep learning is currently used in different industries. Its scope encompasses multiple processes, which have allowed such essential advances and have permeated society so well that today they seem everyday. Its applications can range from the complex, such as financial fraud detection, to slightly simpler processes, such as automatic text translation. The truth is that its expansion is undeniable. Some of the most frequent uses are:
Deep learning in finance
The banking and finance sector has widely adopted deep learning to develop fraud prevention techniques. The use of deep understanding has made it possible to identify patterns in customer transactions to raise alerts in the event of unusual financial behavior.
Deep learning in health
Medicine is another of the sectors where deep learning has allowed significant advances for people. Early detection of diseases such as cancer, osteoporosis, Alzheimer’s, among others, is possible thanks to the analysis of medical images. Artificial intelligence has also allowed assisted diagnosis, even without the intervention of personnel.
Deep learning in marketing
Personalization has become a vital tool for marketing strategies since it allows consumers and potential customers to be assertively reached based on their tastes, interests, and behaviours. Deep learning enables the display of personalized content, which has become a way to create recurrence. This strategy is one of the most used by e-commerce, entertainment applications, and media.
Deep learning and the creative industry
Creativity is a process that deep learning has also come to imitate by learning the structures and patterns that make content popular and then replicating it. In that sense, deep understanding has already been used to compose songs or create content without human intervention.
Deep learning and agriculture
Far from what one might think, agriculture is increasingly using more technological tools to simplify and improve its processes. Deep learning has allowed this sector to identify the level of water in the soil, humidity in the environment, the quality of the ground, or the strength of the wind, to minimize waste in crops.
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Other uses of deep learning
- Chatbots have revolutionized the customer service area, being capable of solving user problems without human intervention and having the possibility of learning progressively to deliver more accurate answers.
- Virtual assistants and robotics, the former are already incorporated into everyday devices such as mobiles; and are capable of understanding and executing voice commands from the user. Meanwhile, robots perform tasks similar to human ones and even make decisions.
- Predictive analysis allows generating more accurate forecasts for sales issues, business results, market evolution, among others.
As has already been demonstrated, deep learning expands across different sectors and industries with endless uses. Although its application extends to small and medium-sized companies and is not only limited to significant technology or Silicon Valley companies, the benefits can indeed be seen more transparently and successfully.
Facial recognition was one of Google’s first innovations in its Google Photos application. This storage tool can classify and sort photos and videos by people or places, thanks to deep learning to recognize shapes and objects.
Voice recognition is another edge of deep learning used by Google to allow users to search in natural language, both from the search engine and from virtual assistants.
The function of translating images with text is another novelty that this company offers its user’s thanks to this technology.
Autonomous vehicles are already a reality, thanks to deep learning. This technology allows the car to collect data about its environment and interpret it to know what actions to take. For Tesla, the goal is for these vehicles to have the ability to predict accidents before they happen.
Where are deep learning and artificial intelligence going?
Data is the gold mine of the technological age. Information is power, and having the necessary machines and technology to analyze as much information as possible is aimed at the short and medium-term.
This translates into two primary focuses: the first is to increase the information processing capacity. The second is to optimize the use of large volumes of data to obtain the greatest possible precision; that is, 99% accuracy will not be enough. 99.99% accuracy is required.
There is a third focus, and that is to count on trained talent to keep up with what these new technologies demand at a business level. The need for professionals with technical training is constantly on the rise, and the supply is not growing at the same rapid rate. Technical and specialized training in Big Data artificial intelligence, among others, becomes increasingly necessary to enhance these technologies and implement them in all business areas.
The current moment can be considered a historical milestone; new technologies and the development of increasingly robust and relevant artificial intelligence systems put humanity in a privileged seat.