Artificial intelligence & machine learning are the computer science’s terms. Artificial intelligence is the study of how to train the systems to get better things than the human can do. Artificial intelligence deals with the simulation of intelligent behavior in the computers. AI is intelligence where you want to add all the capabilities that human contain. Machine learning is possible on its own without the specific program. Machine learning is the application of artificial intelligence which provides the ability to learn automatically for the system. ML does not require human interventions to make changes. Artificial Intelligence and Machine Learning are the terms of computer science. This article discusses some points from which we can differentiate between these two terms.
Artificial Intelligence is one of the computer science’s branches which aims to establish intelligent machines. Machines involved in artificial intelligence are capable of performing tasks that are human intelligence characteristics. John McCarthy, the father of Artificial Intelligence, said: “The intelligent machine’s science & engineering are mostly intelligent computer programs.”
Artificial Intelligence is of two categories.
They are: 1) Artificial General Intelligence
2) Narrow Artificial Intelligence
1) Artificial General Intelligence (AGI):
AGI is also known as “strong AI.” It represents the software’s generalized human cognitive abilities. An Artificial general intelligence system is capable of performing any human tasks.
2) Narrow Artificial Intelligence (NAI):
Narrow AI is also known as “weak AI.” So far Narrow AI is the only AI form achieved by humanity. Single task Performance is one of the features of narrow artificial intelligence.
For example, playing chess, weather forecasts, making purchase suggestions. Working in a limited context is one of the essences of narrow AI & it cannot take duties beyond its field.
Machine learning is the artificial intelligence’s application that provides the ability to learn automatically for the system & to improve without being directly programmed from experience. The focus of machine learning is on the computer program development that can enter data and learn for themselves.
Machine Learning(ML) methods:
1) Supervised learning algorithms
2) Unsupervised learning algorithms
3) Semi-supervised learning algorithms
1) Supervised learning algorithm:
The substantial learning majority mostly uses the supervised learning.
In supervised learning, we have both input and output variables. Suppose B, C & A are input and output variables respectively. The output is equal to the sum of input variables.
A = B+C
Here the goal is addition function. We have inputs (B & C) to find the output for that data.
Supervised learning called so because the algorithm learning process from the dataset training is like a teacher supervising the process of learning. Here we know the exact answers. The algorithm continually makes the forecast on the practice and is corrected by the teacher. When the algorithm achieves, the goal Learning stops.
2) Unsupervised learning algorithm:
In unsupervised learning, we have only input data and no corresponding variables of output. The unsupervised learning goal is to model the structure of latent or information distribution. To learn a lot about data, it is used.
In this algorithm, we don’t have exact answers & no teacher to correct. That’s why it is called as the unsupervised learning algorithm.
To present & discover the data structure which is interested in these algorithms are used.
The other problems in the unsupervised learning are association and clustering.
- Association: This problem is related to suppose a man want to get B & C also leads to getting A.
- Clustering: It is used where we want to invent the data’s inherent groupings.
3) Semi-supervised learning algorithm:
When we have the massive amount of input data but only have some output data. These types of problems are referred to as semi-supervised problems.
These are in between supervised and unsupervised learning algorithms.
Now let’s see the main difference between AI and ML.
People always think that AI and ML are the same, but they are different.
- Artificial Intelligence is nothing but the human intelligence which exhibited by machines.
- Machine learning is a process to achieve artificial intelligence.
- The intelligence in AI is defined as the ability to acquire knowledge.
- The machine learning determines the acquisition of skill or knowledge.
- The goal of AI is to raise the success but not accuracy.
- The ML is used to increase efficiency but not care about the progress.
- Artificial intelligence works like a computer program whereas machine learning learns from the data.
- In AI natural intelligence simulate to solve critical problems. AI is purely the decision making whereas ML learns things from data.
- Artificial intelligence tends to a system to mimic human whereas machine learning involves discovering self-learning algorithms. The optimal solution can be found by using artificial intelligence.
- Machine learning will go for the only solution whether it is optimal or not.
- Artificial Intelligence leads to intelligence or wisdom whereas machine learning leads to knowledge.
Machine learning(ML) is an umbrella term for many algorithms & technologies which is used to learn new information automatically. Today’s artificial intelligence applications are holistic, so they depend heavily on ML to grasp the designs from strong data sets.
Machine learning is a subset of artificial intelligence. In other words, we can say that all machine learning is artificial intelligence, but all artificial intelligence is not machine learning. Though artificial intelligence is extending far, it is not a regulation that imitates the human brain but also constructs intelligence theory to built intelligent machines.