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Are artificial intelligence and machine learning the same?

What exactly is it and how can it revolutionize the world of technology?

Biuse Casaponsa

13 October 2021

Reading time: 3 minutes

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Artificial intelligence and machine learning  (machine learning) are two concepts that appear every time they are heard more and sometimes they can be confused. With this post we want to clarify the difference between the two while defining them. 

Artificial intelligence (AI) is a very broad concept that refers to any tool, algorithm or technology that allows a machine to have typical functionalities of the human brain. We could say that goals of artificial intelligence they would include reasoning, planning, learning, language processing, perception of objects and the ability to move them. These would be short-term goals, and there are already many advances in several of these fields. 

Within artificial intelligence we would have more ambitious objectives such as providing machines with the ability to solve any type of problem, which would be a longer-term objective and which would be within what is called strong artificial intelligence. Whereas when we focus on just one of the human capabilities (such as vision or language), we call it weak artificial intelligence.

Machine learning or machine learning (ML) it is one of the tools to achieve that artificial intelligence. As its name makes us intuit the algorithms of machine learning they are based on learning about a set of examples. For a machine the examples are given with a lot of data. They can easily learn to classify objects, recognize pictures, or make predictions. Models of ML tools are often called predictive models. There are many proposed ML algorithms that work very well, one of the most widely used being neural networks.  Here You can see a list of the most typical ML algorithms.


Artificial intelligence without machine learning

To better see the difference between AI and ML, we propose some examples of artificial intelligence without using machine learning.

  1.  Una máquina con sensores que detecta movimiento y tenga ciertas instrucciones básicas para interpretarlo y reaccionar podría decirse que es inteligencia artificial sin haber usado ninguna técnica de ML. 
  2. Un ordenador donde podemos volcar el conocimiento de muchos expertos de distintas áreas de un cierto campo. Esto le podría permitiría responder a todo tipo de preguntas de ese campo, mejor que uno solo de los expertos. Esto entraría dentro de los objetivos de la inteligencia artificial, en el sentido que emula el razonamiento humano, pero la máquina en sí no ha hecho un proceso de aprendizaje, por lo que no ha usado ML. Este tipo de sistemas informáticos se les llama expert systems, and although they can be useful in some specific fields, artificial intelligence aspires to much more.  


The deep learning revolution

Artificial intelligence is now often associated with ML because since we are able to collect and store large amounts of data, ML techniques have been able to exploit their full potential. This is because with these large databases we can train and make machines learn very well with these algorithms, since we give them many examples. One of the tools that has given the best results is what is known as deep learning, which are algorithms based on neural networks adapted to solve complicated problems with large amounts of inputs (for example in images that contain a high number of pixels).  

En la Figura 1 se puede ver cómo sería la organización jerárquica de estos tres subgrupos de los que hemos hablado: inteligencia artificial, machine learning and deep learning

Diagrama Inteligencia Artificial
Figura 1. Diagrama jerárquico de inteligencia artificial, aprendizaje automático o machine learning y deep learning.


Biuse Casaponsa

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