Contrast Between Artificial Intelligence vs Machine Learning vs Deep Learning

Contrast Between Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence: Artificial Intelligence is the tool used to incorporate human intelligence into machines through a set of rules(algorithm). AI is a compound of two words: “Artificial” meaning something created by humans or non-natural things and “Intelligence” suggesting the ability to understand or think accordingly. Another definition could be that AI is primarily the study of training your machine(computers) to imitate a human brain and it’s thinking capabilities”. AI focuses on three significant aspects(skills): self-correction, learning, and reasoning to obtain maximum efficiency possible.

 

Machine Learning: Machine Learning is the study/process which gives the system(computer) to learn automatically on its own through experiences it had and improve accordingly without being explicitly programmed. ML is an application or subset of AI. ML concentrates on the development of programs so that it can obtain data to use it for themselves. The whole process makes observations on data to recognize the possible patterns being formed and make better future decisions as per the examples provided to them. The principal aim of ML is to allow the systems to learn by themselves through the practices/experience without any human interference or assistance.

 

Deep Learning: Deep Learning is primarily a sub-part of the broader family of Machine Learning which offers the use of Neural Networks(related to the neurons working in our brain) to mimic human brain-like behavior. DL algorithms concentrate on information processing patterns to possibly recognize the patterns just similar to how our human brain does and classifies the information, respectively. DL works on more massive sets of data when related to ML and prediction tools are self-administered by machines.

Below is a table of contrasts between Artificial Intelligence, Machine Learning and Deep Learning:

 

ARTIFICIAL INTELLIGENCE MACHINE LEARNING DEEP LEARNING
AI stands for Artificial Intelligence and is primarily the study/process which allows machines to imitate human behaviour through a particular algorithm. ML means Machine Learning and is the study that applies statistical methods allowing machines to improve with experience. DL means Deep Learning and is the study that applies to Neural Networks(similar to neurons present in the human brain) to mimic functionality just like a human brain.
AI is the more extensive family consisting of ML and DL as it’s components. ML is a subset of AI. DL is a subset of ML.
AI is a computer algorithm which displays intelligence through decision making. ML is an AI algorithm which enables systems to learn from data. DL is an ML algorithm that applies deep(more than one layer) neural networks to examine data and provide output accordingly.
Search Trees and much-complicated math is involved in AI. If you have a clear thought about the logic(math) involved in behind and you can visualize the complex functionalities like K-Mean, Support Vector Machines, etc., then it defines the ML aspect. If you are transparent about the math involved in it but don’t have an idea about the features, so you split the complex functionalities into linear/lower dimension features by adding more layers, then it specifies the DL aspect.
The purpose is to primarily increase possibilities of success and not accuracy The purpose is to increase accuracy not bothering much about the success ratio. It achieves the highest rank in terms of accuracy when it is exercised with a large amount of data.
Three general types/categories Of AI are: Artificial General Intelligence (AGI), Artificial Narrow Intelligence (ANI),  and Artificial Super Intelligence (ASI) Three comprehensive types/categories Of ML are: Supervised Learning, Unsupervised Learning and Reinforcement Learning You  can consider DL as neural networks with a massive number of parameters layers lying in one of the four basic network architectures: Recurrent Neural Networks, Unsupervised Pre-trained Networks, Convolutional Neural Networks,  and Recursive Neural Networks
The capability Of AI is fundamentally the efficiency provided by ML and DL respectively. Less effective than DL as it can’t run for longer dimensions or higher amounts of data.

 

 

More potent than ML as it can quickly work for more massive sets of data.
Examples of AI applications include Fraud Detection, Google’s AI-Powered Predictions, Plagiarism checker, Credit Decision,  Commercial Flights Use an AI Autopilot, etc. Examples of ML applications include Predictions while Commuting, Virtual Personal Assistants: Alexa, Google etc. Videos Surveillance, Email Spam and Malware Filtering.

 

 

Examples of DL applications include Self Driving Cars, Sentiment based news aggregation, Colourisation of Black and White images, Image analysis and caption generation, Adding sounds to silent movies. Etc.

 

 

 

Conclusion

 

Artificial intelligence has many exceptional applications that are changing the world of technology. While building an AI system that is ordinarily as intelligent as humans remains a dream, ML already allows the computer to outperform us in computations, pattern recognition, and anomaly detection.

Conclusively, deep learning is a subset of machine learning, applying many-layered neural networks to answer the hardest (for computers) problems.

 

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