AI, Machine Learning & Deep Learning… Whats The Difference?
There are many misconceptions related to the concepts Machine Learning, Deep Learning and Artificial Intelligence(AI). People often confuse in these terms and use them in similarly ways. But these terms are different from each other and so here are some short definitions:-
AI means getting a computer to mimic human behavior. Machine learning is a subset of AI and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
Artificial Intelligence
Artificial Intelligence (AI), as the name suggests is the intelligence created by humans. It constructed as complex machines using computer properties and performing various actions just like we the humans.
AI is the broadest way to think about advanced, computer intelligence. In 1956 at the Dartmouth Artificial Intelligence Conference, the technology was described as such: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
The goal was to get computers to perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems. Artificial intelligence, then, refers to the output of a computer. The computer is doing something intelligent, so it’s exhibiting intelligence that is artificial.
The term AI doesn’t say anything about how those problems are solved. There are many different techniques including rule-based or expert systems. And one category of techniques started becoming more widely used in the 1980s: machine learning.
Machine Learning (ML)
Most of the people consider it to be Artificial Intelligence, but that is not true as Machine Learning is a focused part of the over-riding Artificial Intelligence. ML is a part of AI and is the best tool so far to analyse, understand and identify a pattern in the data.
One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention. ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks.
For instance robots can independently learn from the data provided to them. Also in the human world when you get recommendations on shopping sites like Google, or Facebook you get these suggestions according to your interests. New machine learning algorithms has become an essential part of some system and it has altered the way of watching shows and movies on web channels like Netflix and Amazon Prime.
It is done with machine learning algorithms which are developed in the way to analysing the recent searches, history, and other information. This technique also influences the marketing and banking sectors.
Feed an algorithm a lot of data on financial transactions, tell it which ones are fraudulent, and let it work out what indicates fraud so it can predict fraud in the future. Or feed it information about your customer base and let it figure out how best to segment them.
Deep Learning
Deep learning has made machines to work and think like just humans. Deep learning is the new state of the art in term of AI. In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.
Put simply, deep learning is all about using neural networks with more neurons, layers, and interconnectivity. We’re still a long way off from mimicking the human brain in all its complexity, but we’re moving in that direction.
Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning because it makes use of deep neural networks. The machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model.
Deep Learning has enhanced the expertise of users. The best example of deep learning is an automatic car. And so when you read about advances in computing from autonomous cars to Go-playing supercomputers to speech recognition, that’s deep learning.
Summary
Artificial intelligence is imparting a cognitive ability to a machine. Early AI systems used pattern matching and expert systems.
The idea behind machine learning is that the machine can learn without human intervention. The machine needs to find a way to learn how to solve a task given the data.
Deep learning is the breakthrough in the field of artificial intelligence. When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation.
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