AI Vs Machine Learning
Artificial intelligence is a technology that empowers a mechanism to simulate human behavior. Machine learning is a subclass of AI that allows a machine to automatically learn from past data without openly programming it. The goal of AI is to make a smart computer system like humans to resolve multifaceted problems.
Artificial intelligence and machine learning are the parts of computer science that are correlated with each other. These two technologies are the most trending technologies that are used for creating intelligent systems. Although these are two related technologies and sometimes people use them as synonyms for each other, but still both are 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 that 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 the Buttressing learning procedure and deep learning neural networks. AI is being used in multiple places such as Siri, Google’s 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 make 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 procedures that absorb on their own using historical data. It works only for exact domains if we are creating a machine learning model to sense pictures of dogs, it will only give results for dog imageries, but if we provide new data like cat images then it will become unresponsive. Machine learning is being used in various places such as for online recommender systems, Google search procedures, Email spam filters, Facebook Auto acquaintance tagging suggestions, etc.
It can be divided into three types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
Related Post