Do you get lost when they talk about Artificial Intelligence? The 15 terms you should know

Nobody said that Artificial Intelligence was easy. If we talk about AI, we are not only talking about applications or web pages that automatically generate text or images based on a description, but we are talking about much more. If we want to expand our knowledge about what Artificial Intelligence is and how it works, we must have some basic knowledge.

We can easily learn this knowledge through the most used terms in this field and that we show you below.

  1. Algorithm. The algorithm is the basis of the operation of any system since it includes the instructions and rules that the AI ​​must use to answer a question correctly.
  2. machine learning (Machine Learning). Unlike deep learning, it makes inferences on large amounts of uncataloged information to find patterns and make predictions.
  3. reinforcement learning. It is a machine learning model in which Artificial Intelligence is capable of making decisions through trial and error, receiving rewards or punishments that help it improve its performance over time.
  4. deep learning. Deep Learning, by its name in English, is a branch of Artificial Intelligence that uses artificial neural networks to learn and analyze large amounts of data, allowing them to perform complex tasks such as voice recognition and processing of an image and natural language.
  5. supervised learning. It is a machine learning model that is trained based on previously labeled data to make predictions from large amounts of data.
  6. big data. A very popular term that describes large data sets that are difficult to analyze using traditional methods. Big Data is used to analyze large sets of information to extract the most important and make decisions based on them.
  7. chatbot. This is the interface that allows us to interact with an AI through text and/or voice commands and are capable of understanding and generating responses in the same way as a human.

ChatGPT uses

  1. data science. This is in charge of extracting information from large amounts of data using scientific systems and algorithms and covers a wide range of activities such as data collection and visualization as well as a predictive model to solve complex problems.
  2. cognitive computing. It is a field of Artificial Intelligence that develops systems that mimic human abilities such as learning, reasoning, perfection, and problem solving.
  3. Generative Artificial Intelligence. This is the one that describes the systems, methods and algorithms that allow Artificial Intelligences to generate text, audio, images or videos based on a description and draw conclusions with the interaction of humans.
  4. Data mining. In a similar way to cryptocurrencies, data mining in AI is the process of gaining knowledge based on large amounts of data, data that is analyzed to identify relationships and patterns to improve the way it processes information and offers correct answers.
  5. natural language processing (NLP). It is the ability of the AI ​​to interpret, understand and answer a question in a way that is readable by humans.
  6. pattern recognition. It is the ability of Artificial Intelligence to correctly identify and interpret patterns in the data it analyzes.
  7. neural network. A neural network, related to deep learning (deep learning) is a computational model inspired by the human brain consisting of interconnected nodes (also called neurons) organized in layers. These share information with each other, which allows you to learn patterns to make decisions, being one of the key components of machine learning models.
  8. recurrent neural network (RNN). It is a type of neural network that processes sequential data through feedback connections and is capable of storing previous entries in memory, which allows a conversation to be held on the same topic, being very useful in the natural language processing function.

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