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Difference between AI vs Machine Learning vs Deep Learning

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Head to Head Comparison Between AI vs Machine Learning vs Deep Learning (Infographics)

Below are the lists of points described and the key differences between AI vs Machine Learning vs Deep Learning:

Key Differences Between AI vs Machine Learning vs Deep Learning

One of the major key differences between artificial intelligence, machine learning, and deep learning is their approach to processing data. Deep learning uses neural networks and is inspired by the functioning of the human brain. On the other hand, Machine Learning relies heavily on algorithms humans design to reach the desired outcome. Lastly, Artificial intelligence depends on predefined scenario-based options and rules fed into its system.

Deep learning is the latest development in the field of artificial intelligence and is gaining rapid popularity due to its ability to provide more accurate results when compared to machine learning.

In the case of deep learning, it takes longer to train its neural network when compared to machine language.

Deep learning requires high-performing systems to process the data through its many layers of neural networks. In the case of machine learning, no such requirements are needed. It can perform on any ordinary system.

Deep learning can process large size data sets. Whereas machine learning works better with small to medium baize data sets.

Comparison Table of AI vs Machine Learning vs Deep Learning

Below are the differences between AI vs Machine Learning vs Deep Learning:

Basis of Comparison Artificial Intelligence Machine Learning Deep Learning

Basic difference Artificial Intelligence takes actions based on its internal programming. This programming is done on the basis of conclusions derived from prior observations and a pre-defined set of rules. Machine learning involves using algorithms to process data. Helping one to predict certain outcomes or to reach a conclusion using complex algorithms. Deep learning helps interpret data, giving one a better understanding of data and its behavior. Due to its use of the deep neural network in interpreting data, it’s named deep learning.

Relation to one another Artificial intelligence constitutes both deep learning and machine learning. Machine Learning is a subset of Artificial intelligence or it can be said to be a part of artificial intelligence. Deep learning is a subset of Machine Learning. Therefore we could say Deep learning is a part of machine learning as well as artificial intelligence.

Hardware Requirements Hardware requirement totally depends on the task being performed as artificial intelligence can range from executing simple tasks to very complex tasks. Could operate on any regular machine, and doesn’t require any predefined system specifications. Needs a powerful machine containing high-performing CPUs and a larger RAM size as it performs a large number of matrix multiplications.

Working Process Artificial intelligence makes use of a set of pre-defined rules or pre-defined options that are set in case of the occurrence of a certain scenario. It proceeds with the result obtained that better fits the situation. Machine learning works on the basis of Algorithms. The algorithm is trained to distinguish by designing the algorithm as per the required outcome. Often a manual intervention is needed as and when the algorithm provides the incorrect outcome. Deep Learning works on the basis of the neural network. It consists of layers of neural networks. These are similar to the workings of the neural network present in an actual human brain.

Presentation of Data Artificial intelligence work with data from the end-user. The data could be fed as part of audio, video, or any other means and is always unstructured. These mostly interact with humans and provide a response. Machine Learning is fed structured data. Its algorithms are written/developed to make decisions based on the structured data (that is nothing but labeled data) to reach a certain conclusion. Deep learning works with unstructured data as it relies on Artificial Neural Networks to process data.

Performance with respect to the Size of a Data set Performance is purely dependent on its capabilities as an end product. That is its performance depends on how well its knowledge base is, how clearly its protocols are defined, and also how efficient its algorithm is in providing the desired outcome. Performs better with smaller data sets when compared to deep learning. Performs well even when fed with larger data sets when compared to machine learning.

Time Efficiency Artificial intelligence can provide an output in a matter of minutes or take the time up to a few weeks. Therefore time consumed is an important feature that needs to be considered when developing the underlying logic of artificial intelligence. However, it’s directly proportional to the complexity of the task being performed. Machine Learning performs better and provides faster outputs. It doesn’t take longer than a few hours at most. When it comes to time consumed to provide an output, Deep learning tends to take longer due to its neural network framework. It can take up to weeks.

Conclusion

Artificial intelligence has its roots way back in the 1950s. It was John McCarthy who had originally found the term “Artificial Intelligence”. Over the years artificial intelligence has evolved dynamically. However, it wasn’t until the 21st century that artificial intelligence gained popularity. Today, artificial intelligence has given rise to many different fields within itself that specialize in processing the input data in different ways such as machine learning and deep learning. In this article, we have seen how these fields differentiate from one another.