Machine Learning, Artificial intelligence (AI) and Deep Learning are taking the world by storm, dominating conversations about how machines can replace humans by providing a competitive advantage to businesses. The World is currently preparing to enter the fourth industrial revolution — the rise of the “intelligent machine.” At the heart of this revolution is Artificial Intelligence (AI), Mimicking human cognitive functions like problem-solving, learning and decision making using algorithms. From speed to efficiency, AI offers an abundance of benefits. Numerous sectors, including healthcare, automotive, defence and retail have already witnessed the game-changing impact of AI. 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.
So what is artificial intelligence and what does the term matching learning and deep learning mean?
What is Artificial intelligence?
First coined in 1956 by John McCarthy, The goal of artificial intelligence was to get computers to learn cognitive functions and perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems… There are other forms of AI that include emotional and social intelligence. the type of AI we use now is narrow for it can only perform certain actions. Machine Learning and Deep learning are used in the creation of AI.
What Is Machine Learning?
Machine learning has become a rapidly growing form of AI research. Using low-cost computational hardware resources to solve problems which so far have relied on human brain-power. To understand the term machine learning we have to discuss it’s categories first Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Supervised Learning: Where we teach the machine what to learn, Consider a student sitting in a classroom wherein his teacher is supervising his ability to solve problems based on his answers are. Likewise, in Supervised Learning, a model can learn from an input labelled as a dataset to provide the result of the problem easily. Two types of problems are being dealt with supervised learning classification problems where an algorithm helps predicting a discrete value and regression problems, Regression problems where the output variable is a real or continuous value.
Unsupervised Learning: Where the machine will find what to learn, Let’s take an example of a baby trying to talk by mimicking his parents. Unsupervised Learning is where you do not need to supervise the model, it finds all kind of unknown patterns in data. Clustering and Association are two types of Unsupervised learning.
Reinforcement Learning: Where the machine learns from previous mistakes at every step, the algorithms learn to react to an environment on their own.
we also can’t speak of Machine learning, Artificial Intelligence and Deep learning without knowing what the word algorithm means since it is heavily used to teach machines. From a simple process such as solving simple math to complex operations like stock trading, The algorithm is simply a set of a step by step instructions designed to solve a problem or to execute a task. for example, You can consider a cake recipe an algorithm for making cakes. In conclusion, a good way to think of algorithms is as mini instruction manuals telling computers how to manipulate given data or complete given tasks. There are 8 different machine learning algorithms Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forests, K-Means Clustering and Principal Components Analysis.
What Is Deep Learning?
Deep learning is useful when trying to learn patterns from unstructured data it is a method of machine learning that incorporates neural networks in successive layers in order to learn from data in an iterative manner.
Neurol Network architectures are used in Most deep learning methods which is why deep learning models are often referred to as deep neural networks. By “deep” we refer to the hidden layers in the neural network a traditional neural network can contain 2 to 3 hidden layers however deep networks can have as many as 150. We train deep learning models by using large sets of labelled data without the need for manual feature extraction and directly from the data.
Conclusion:
Today, machine learning techniques are beginning to become
popular in a variety of specialized environments. However, I think we should pause and re-think creating machines that could possibly be smarter than us someday. I want to end this article quoting Elon Musk when asked about the future of AI.
“The biggest issue I see with so-called AI experts is that they think they know more than they do, and they think they are smarter than they actually are. This tends to plague smart people. They define themselves by their intelligence and they don’t like the idea that a machine could be way smarter than them, so they discount the idea — which is fundamentally flawed.”
