Machine learning and deep learning are two concepts related to artificial intelligence. Thanks to the development of the digital age, both branches are acquiring enormous importance. But what do they consist of?
Before explaining these two technologies, it is necessary to remember the definition of their origin: Artificial Intelligence. This resides in the capacity of a machine to process the data it captures, which in turn are the result of previous experiences. This processing is similar to the functioning of the human brain, which captures information and transforms it to generate knowledge.
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What Are Machine Learning And Deep Learning?
First of all, machine learning, as a derivative of AI, involves the creation of algorithms that can modify themselves without human presence.
Machine learning is a data analysis method based on the premise that our systems learn from that data. Through such a method, plans will be able to identify patterns and make decisions without any intervention on our part.
On the other hand, deep learning is a type of machine learning whose function is to train a system to learn by itself. This competition is possible by recognising patterns and executing tasks such as those that we human beings do. As a relevant fact, this branch of AI uses a specific class of algorithms called neural networks.
Although the algorithms are created and work similarly to machine learning, multiple layers of neural networks are responsible for individually providing a different interpretation of the data. These networks have the purpose of trying to imitate the function of the neural networks of our brain. This includes voice recognition, object detection, and image identification. Voice assistants such as Amazon’s Alexa or Apple’s Siri are based on this technology. Even biometric recognition systems for fingerprints, face, voice, etc., also have this type of technology.
Machine Learning And Deep Learning Have Different Capabilities.
A deep learning model is designed to continuously perform data analysis while maintaining a logical structure similar to a human being. To achieve this type of analysis, deep learning must use layers of algorithmic systems, those mentioned above, artificial neural networks. These networks are the ones that allow much more advanced knowledge than the basic machine learning models. Deep learning facilitates the automation of training processes and is capable of creating its criteria automatically, altogether dispensing with human intervention.
In short, machine learning and deep learning are almost the same, as they work in the same way but have different capabilities. Although the basic models of machine learning are continually evolving, their functions still require monitoring on our part. Suppose an artificial intelligence algorithm gives an incorrect prediction. As a result, we will have to intervene and apply the necessary adjustments. By having this model, an algorithm will be able to determine if a prediction is incorrect by utilizing its neural network.
Advantages Of Machine Learning and Deep Learning
Many organizations have basic or advanced Artificial Intelligence applications, and their use continues to spread.
Regardless of the productive sector or the size of these companies, implementing this technology helps solve common day-to-day problems to the most complex. For this reason, this technology has a very positive impact on efficiency and profitability.
In particular, companies that manage large amounts of data must rely on machine learning and deep learning; since these resources can be used in different areas, from finance and health to marketing and sales.
We can summarize part of the advantages of AI in these areas:
- Speed in the management and processing of data and identification of relevant information.
- Ability to analyze consumer behavior with greater precision.
- Fraud detection and prevention, specifically in the banking and insurance sector.
Even machine learning and deep learning support making the right decisions in companies. Likewise, they increase the capacity for efficient and intelligent work, reducing the percentage of human error and adding competitive advantages.
How Can Machine Learning And Deep Learning Be Helpful in Our Company?
Machine learning and deep learning contribute decisively to our company obtaining scalability, more excellent performance, and cost and time savings. In addition, these technologies can also provide the following benefits:
- They personalized customer service. It allows analyzing user preferences so that personalized products can be offered automatically. In this way, the perception that customers have of our company is improved, thus enhancing loyalty. For example, platforms such as Netflix, YouTube and Spotify constantly use this technology to suggest other content based on what we have enjoyed.
- Process automation. Of course, one of the most relevant contributions of the two technologies that we analyze is the automation of routine tasks. The latter absorbs a lot of time and effort from human talent and does not provide added value. Using machine learning, our systems can detect the processes they must deal with.
- Reduce errors. The automatic learning of the management systems applied in the organization means that the mistakes made are not repeated. The longer it stays in the system, the more resilient it will be.
- Preventive actions. Based on the above, machine learning tools can prevent bugs and errors. Artificial Intelligence can exclude any action that compromises or puts the development of products or services at risk.
Other Important Uses
- Cybersecurity. Undoubtedly, the contribution of this technology to the protection of networks, systems and terminals of organizations against risks of cyberattacks is significant. It should be noted that most malware uses similar code, so the use of machine learning can prevent their meddling.
- Fraud detection. Thanks to the technologies we are dealing with, it is possible to detect which transactions are legitimate and which are not accessible. It is even feasible to reveal the mismanagement of resources. Such a function is achievable when a pattern is assigned to financial movements.
- Medical diagnoses. When implemented in the technological tools of the health system, these technologies help insurers be more intuitive about possible health problems, depending on the frequency of medical consultations. Apart from that, these technologies offer more reasonable costs and recommend different medication options, among others.
- Improves the security and integrity of information. Cloud storage is another service that facilitates these two strands of AI.