Integrating Artificial Intelligence in machine learning

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Artificial Intelligence in machine learning

The future face of modern technology is Artificial Intelligence. It is a field of computer science which creates systems that can mimic human intelligence. One of the most important manifestations of Artificial Intelligence is Machine Learning. It means that the machines use AI to learn predictive behaviours based on the observed and experienced data without any specified pre-fed equations. In this way, the machines can ‘act’ and ‘think’ as humans would in a certain situation.

The wonders of Machine Learning:

Machine Learning involves the use of Artificial Intelligence to extract knowledge or information from a data set without explicit programming. It uses past experiences and observations to deduce logical conclusions and results.

The goal of integrating Artificial Intelligence in Machine Learning is to make smart computers that can work like human minds to solve the problems at hand and reduce the workload of humans.

Machine Learning Implementations:

There are various implementations of Machine Learning. The Google search algorithms, Facebook pictures’ auto-tagging options as well as the online recommendation systems, these are all the wonders of Machine Learning.

Types of Machine Learning:

There are three categories of machine learning.

  • Supervised machine learning
  • Unsupervised machine learning
  • Reinforcement learning
  1. Supervised Machine Learning:

This ML algorithm learns from a pre-fed dataset classified into different sub-categories. It uses the classifications from the previous dataset to make observations and deduce results about the new data being fed into the system.

There are two types of Supervised Learning; Classification and Regression.

  • Classification – involves classifying the dataset into different labelled sub-categories.
  • Regression – involves the information about how one variable affects another variable in a dataset.

 

  1. Unsupervised Machine Learning:

In this Artificial Intelligence Machine Learning algorithm, the machine draws inferences from the data which has not previously been labelled under any sub-categories.

Unsupervised Machine Learning works through ‘Clustering’.

Clustering means that the machine uses AI to divide the data into specific clusters or groups based on the similarities and differences in the dataset.

  1. Reinforcement Learning:

Another type of machine learning is where the machine learns through positive and negative feedback.

In this algorithm, the machine learns ideal behavior from the feedback it gets to maximize performance and efficiency. For example, in a Paceman game eating the food will gain points and this is positive feedback. When the same Pacman crashes into a monster, it dies. It is negative feedback, reinforcing the idea that the Pacman has to eat more food and avoid the monsters in order to deliver efficient performance.

This is how Reinforcement machine learning works.

Conclusion:

Combining Artificial Intelligence with Machine Learning is opening new doors of success for the technical world, enabling us to process larger volumes of data in comparatively shorter periods, improving the overall efficiency and efficacy of machine systems.

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