AI Vs Machine Learning
Artificial intelligence is a technology that empowers a mechanism to simulate human behaviour. Machine learning is a subclass of AI which allows a machine to automatically learn from past data without programming openly. The goal of AI is to make a smart computer system like humans to resolve multifaceted problems.
Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems. Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two diverse terms in numerous cases.
Below are some key differences between AI and machine learning along with the outline of Artificial intelligence and machine learning.
Artificial Intelligence
Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence”, which means “a human-made thinking power.” Hence, we can explain it as, “Artificial intelligence is a technology using which we can generate intelligent systems that can pretend human intelligence”.
The Artificial intelligence system does not necessitate to be pre-programmed, instead of that, they use such procedures which can work with their own intelligence. It involves machine learning processes such as Buttressing learning procedure and deep learning neural networks. AI is being used in multiple places such as Siri, Googles AlphaGo, AI in Chess playing, etc.
Based on capabilities, AI can be classified into three types:
- Weak AI
- General AI
- Strong AI
Machine Learning
Machine learning is about extracting knowledge from the data. It can be defined as, “Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed”.
Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. ML uses a massive amount of organised and semi-structured information so that a machine learning model can produce accurate result or give predictions founded on that data.
Machine learning works on procedure which absorb by its own using historic data. It works only for exact domains such as if we are creating a machine learning model to sense pictures of dogs, it will only give result for dog imageries, but if we provide a new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender system, for Google search procedures, Email spam filter, Facebook Auto acquaintance tagging suggestion, etc.
It can be divided into three types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
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