How Machines Learn and Why Artificial Intelligence Is Not Intelligent

Written by Alice Jones

Alice Jones is a writer and journalist engaged with several online publishers and custom essay writing services. She is from San Francisco, CA. She graduated from the University of San Francisco and got a Master’s degree. Alice provides custom research paper writing service to students with a focus on such topics as business, marketing, and freelance.

August 8, 2020

The popularity of machine learning and artificial intelligence has continued to grow for many years and has finally gotten the spotlight. But what are these terms?

Artificial Intelligence (AI) has to do with machines being able to simulate human intelligence, so the machines are programmed to mimic humans’ thinking and actions. This term can also be used for devices that show traits such as problem-solving, learning, and other abilities associated with the mind of humans.

Machine Learning (ML) is a class of AI that makes software applications more accurate when making precise predictions on their own without being programmed explicitly. The algorithms of machine learning use past data as input based on which new output values are predicted.

One of the most prevalent usages of machine learning today is recommendation engines, which a site like Amazon uses to recommend products you might need on the back of past ones you’ve bought. It is also used popularly in business process automation, fraud detection, predictive maintenance, malware threat detection, and spam filtering.


According to a professional paper writer at dissertation services, the categorization of machine learning is based on how the algorithm learns to get better accuracy in its predictions. Through reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning, these are the four ways in which machine learns. A data scientist will choose the right algorithm, depending on the type of data the device predicts.

  • Supervised learning: this class of machine learning requires that a data scientist gives the algorithms an input that includes labeled training data. The variables which the algorithm needs to assess and correlate are also defined. This gives birth to a specific output. In this case, the algorithm has specified input and output.
  • Unsupervised learning: this class of machine learning includes algorithms that are trained on unlabeled data. The algorithm has to scan through all the data sets to see if there is any meaningful correlation between them. The algorithm trains on this data and gives a predetermined recommendation or prediction as output.
  • Semi-supervised learning: this is another class of machine learning that seems to combine both the supervised and unsupervised types of machine learning. In this case, the data scientist may need to supply the algorithm with training data like supervised learning. However, the algorithm can explore data in different ways and try to have an independent understanding of the training data that it received.
  • Reinforcement learning: this class of machine learning is engaged in teaching the machine to finish a process with multiple steps with a clearly defined set of rules. The data scientist may program the algorithm to finish the task and give it a negative or positive cue while working on ways to complete the job. However, for the more substantial part of it, the algorithm has to decide which steps to take independently along the way.



The different classes of machine learning work in different ways to achieve the result that they give.

Supervised machine learning

In this case, the data scientist plays the role of training the algorithm with labeled data (which is the input) and the desired output. This type of algorithm is best suitable for some particular tasks, some of which are listed below.

  • Binary classification: this involves dividing data into two classes.
  • Multi-class classification: this involves having to have to choose from more than two options.
  • Regression modeling: this involves the prediction of continuous values.
  • Ensembling: this involves the combination of multiple machine learning model predictions to give a more accurate forecast.

Unsupervised machine learning

The algorithms of unsupervised machine learning don’t need labeled data. The algorithms scan through the unlabeled data for patterns useful for grouping the data points into different subsets. Almost all deep learning types are unsupervised algorithms. These algorithms are suitable for some particular tasks, some of which are listed below.

  • Clustering: this has to do with dividing the data into two groups based on their similarity.
  • Detection of an anomaly: this has to do with identifying particular points within the set of data.
  • Association mining: this involves identifying the groups of items within a data set that frequently occurs together.
  • Dimensionality reduction: this involves the decline of the number of variables in a set of data.

Semi-supervised machine learning

This machine learning also involves a data scientist giving the algorithm some labeled training data. The algorithm takes this data to learn some dimensions which it then applies a different unlabeled data. As the algorithms train with the labeled data, they get better with their performance.

However, it can be expensive and time-consuming to label data sets; that’s why this type of machine learning is essential because it provides a middle ground between the efficiency and performance of supervised learning.

It can be used in areas such as:

  • Machine translation: here, the algorithm learns to translate language with a dictionary of words.
  • Fraud detection: this involves the identification of fraud cases with a few positive examples.

Reinforcement learning

This machine learning requires the algorithm to be programmed with a particular goal and a set of prescribed rules to accomplish the goal. The algorithm is also scheduled to look for positive rewards for completing a move towards the goal and punishments for completing an action that takes it farther from the target. Reinforcement learning is used in

  • Video game: the bots are taught to play the game.
  • Robotics: robots learn to carry out tasks using this technique.

Machine learning is a big part of artificial intelligence, and these are the four different ways in which machine learning. The concept of artificial intelligence in which the machines are taught to mimic the thinking and actions of humans in itself invalidates any knowledge in there.

Undoubtedly, it’s an improvement in our relations with and the usefulness of machines, but it’s not intelligent if all it does is mimic humans, and it also has to be taught in some ways to mimic.


The idea of artificial intelligence is right, and it is a much-needed technology, but calling it that name sounds wrong. It’s not intelligent if it mimics human intelligence. I’m not a monkey for mimicking a monkey!

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