Computer science is the aspect of artificial intelligence and machine learning that is associated with each other. These technologies are the most cutting-edge technology used to build intelligent systems.
While these are two similar technologies and often people use them as synonyms for each other, in various situations these are still the two distinct words.
We may distinguish both AI and ML on a wide level as:
AI is a broader term for creating smart machines that can mimic human thought skills and behaviour, while machine learning is an AI technology or subset that allows machines to learn from data without explicit programming.
Below are some of the key distinctions between Machine learning and artificial intelligence along with a description of AI and ML:
Artificial Intelligence (Ai)
Artificial intelligence is an informatics field that makes a computer capable of mimicking human intelligence. It consists of two terms ‘Artificial’ and ‘Intelligence,’ meaning ‘a force of thought created by man.’ Therefore we may describe it as,
Artificial intelligence is a technology that we use to build smart systems that can mimic human intelligence.
Rather they use these algorithms that can work on their own intelligence, the Artificial Intelligence system does not require pre-programming. This includes algorithms in machine learning such as algorithms in pattern recognition and neural networks learning. AI is used in several places like Siri, Google? S AlphaGo, AI playing in Chess, etc.
AI can be divided into three categories, based on capabilities:
- Weak AI
- General AI
- Strong AI
At the moment we are focusing on weak AI and general AI. AI’s future is Strong AI, for which it’s said it’ll be smart than men.
Machine learning
Machine learning is about extracting the data from the information. Can be described as,
Machine learning is a subfield of artificial intelligence that allows computers to learn from past data or experiences without being programmed directly.
Machine learning helps a computer system to simulate or make certain decisions using historical data without explicit programming. Machine learning uses a vast amount of semi – structured and structured data to allow a model of machine learning to produce accurate results or make predictions based on those data.
The learning of computers relies on algorithms that learn from them? With historical evidence, s own. This only works with similar domains such as if we are developing a machine learning model to detect dog pictures, it will only produce results with dog pictures, but if we have new data such as cat pictures then it will become unresponsive. Machine learning is used in various places such as online recommender program, search algorithms for Google, email spam blocker, the recommendation for Facebook Auto-tagging contacts, etc.
It is divisible into three types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
Main distinctions between AI and ML
