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Basics Concepts of AI (Artificial Intelligence)

Artificial Intelligence is a canopy concepts of AI that encompasses any technology that enables computers to mimic human abilities. In this sense, areas such as Robotics, Parallel Computing, Natural Language Processing, Machine Learning, and Deep Learning are part of AI. All these areas seek, in their way, to automate activities that humans do naturally and intuitively.


1. Artificial intelligence

Artificial Intelligence is a term that raises to the ability of a computer or machine to perform tasks or make decisions, just like humans. Its developers intend to reproduce human attributes such as creativity, logical reasoning and knowledge acquisition at different levels. Let’s look at virtual assistants like Siri or Alexa, the vast majority of social media platforms. They demonstrate how Artificial Intelligence can automate specific tasks that only humans could perform before.

2. Machine Learning

In simple terms, Machine Learning or Automatic Learning is a subset of Artificial Intelligence. Its primary lies in the idea that computer systems can learn on their own from data obtained by performing previous tasks and past experiences. It means that an AI device does not need to be pre-programmed every time it needs to work on a mission.

3. Supervised Learning

Machine Learning divide into three main areas, the first of which is Supervised Learning. This type of learning involves observations or labelled data points. A Supervised Learning process models the relationship between independent variables, that is, the features, and a dependent variable, the target or the label, giving a data set with observations.

To better understand this, I give you as an example a model that predicts the price of a house based on the years of construction, location and size. In this case, the characteristics would be age, location and height, while the price is the objective.

4. Unsupervised Learning

The following coverage area within Machine Learning is Unsupervised Learning. In this type of learning, no labels include for the observations. An Unsupervised Learning algorithm finds the fundamental structure or patterns among a set of observations.

To see it practically, let’s see an example; it can be a retail business that divides its customers into groups based on their purchasing behaviour. There is no tag associate with clients. Instead, the Unsupervised Learning algorithm expects to find those labels.

5. Reinforcement Learning

The last type of Machine Learning is Support Knowledge, based on the action-reward principle. An agent learns to achieve a goal by iteratively calculating the reward for its actions.

It can even consider learning from interaction. It is why Reinforcement Learning is sometimes said to be similar to how we learn from our mistakes. An agent interacts with the environment to spread its goal and evaluates the result of its actions. The most common applications of Reinforcement Learning are computer games and robotics.

6. Classification

Classification is a Supervised Learning technique that deals with discrete or categorical objective variables. For example, it is detecting whether an email is a desire or not would be a classification task. Suppose the target variable only has two possible values. In that case, it calls a binary classification, whereas if the variable contains more than two values ​​or classes, it is called a multiclass classification.

7. Clustering or Grouping

Clustering or grouping is a technique link to Unsupervised Learning and groups the observations so that the statements of the same group are more similar to each other than those of other groups. Precisely those groups are known as clusters. Unlike the classification we explained earlier, the observations here do not have labels. An example of clustering is to group customers according to their purchasing behaviour.

8. Deep Learning

If we want a simple and accurate definition, it says that Deep Learning takes Machine Learning to a higher level. But let’s see a more technical description: Deep Learning or Deep Learning is a subset of Artificial Intelligence and refers to the ability of a system to take unstructured data from multiple sources. Analyse-it and apply it to solve new problems. Still, to carry out all this, it uses artificial neural networks, the vast majority of them modified, that what it does is similar to the behaviour of the networks we have in our brain.

9. Artificial neural networks

Artificial Neural Networks refer to a system or algorithm used for deep learning that mimics the functioning of neural circuits in the human brain, for example, in making sense of things and events.

Let us now explain several more specific concepts in developing Artificial Intelligence projects, but they are essential to understand and always keep in mind.

10. Data set

It is the raw material of any prediction system. The historical data used to train the system detects the patterns. The data set or dataset, as it is sometimes also known, is made up of instances, characteristics or properties.

11. overfitting

Overfitting occurs when a model attempts to fit the training data so well that it cannot make a good prediction when we feed in new observations. Overfitting usually happens when we want our model to get a near-perfect forecast, so we push it to such an extent that the model fits perfectly with the training data.

12. Underfitting

Underfitting would be the opposite of overfitting. An underfitted model does not capture enough detail in the training data, so it performs poorly on either the training or test data.


Once we know the basic concepts of AI, it’s easy to understand why it’s such a revolution. So AI ​​can perform all kinds of tasks. Optimising live power consumption or performing quality checks and adjustments while production is in progress are examples of its potential.

Also Read: Venture Capitalist (VC) – Advantage, Disadvantages and More

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