What are Artificial Intelligence, Machine Learning and Deep Learning?
AI is becoming a kind of buzz word. Everybody is talking about it. And this is actually justified. Because AI has been actually making breakthroughs in the last few years. So, this article will go over some basics of AI.
Artificial Intelligence(AI): Intelligence is the ability to learn & solve problems and artificial intelligence is the intelligence exhibited by machine or software. It can also be defined as “ Study and design of intelligent agents, where intelligent agent is a system that perceives its environment and takes actions that maximize its chances of sucess.” this definition is coming from Russell and Norvig the authors of the famous artificial intelligence book. While there are a lot of different ways to think about AI and a lot of different techniques to approach it, the key to machine intelligence is that it must be able to sense, reason, and act, then adapt based on experience.
Artificial intelligence was born in the 1950s, as a handful of pioneers from the nascent field of computer science started asking if computers could be made to “think” . As such, AI is a very general field which encompasses machine learning and deep learning, but also includes many more approaches that do not involve any learning. In fact, for a fairly long time many experts believed that human-level artificial intelligence could be achieved simply by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as “symbolic AI”.
Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, or language translation. A new approach to AI arose to take its place: machine learning.
Machine Learning(ML): Giving computer ability to learn to make decisions from data without being explicitly programmed is known as machine learning. Tom Mitchell defines ML as “A computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E.”
In machine learning, learning algorithms build a model from training set (data), which they can improve on as they are exposed to more data over time. Then this model is used forprediction. There are four main types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning.
Deep Learning: is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. These are algorithms use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts.
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