Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence is the future. For a common man, artificial intelligence is a science fiction and is already a part and parcel of our daily lives. Machine learning and deep learning are the two buzzing terms associated with artificial intelligence. Deep learning, machine learning and artificial intelligence are a set of Russian dolls nested with each other beginning with the smallest and working out. This article will help in understanding artificial intelligence, machine learning and deep learning and the difference among them. Also explains how artificial intelligence and Internet of things are entwined to each other giving which helps in exploring cutting-edge technological advancements.

Artificial Intelligence

"The science and engineering of making intelligent machines" is one of the definitions coined by the godfather of AI-John McCarthy. Artificial intelligence (AI) is the branch of computer science dealing with simulations of intelligent behavior in computers. There are a million ways to simulate human intelligence and some simulations are better than others. AI can be a set of statements or complex statistical model mapping raw sensory data to symbolic categories. Taken together these statements are sometimes called as rule engines, expert system, knowledge graphs or symbolic AI. In general AI involves the machines that perform tasks that are characteristic of human intelligence. This includes things like planning, understanding language, recognizing objects, and problem-solving.

AI broadly branches into two- general and narrow. General AI empowers the machine the same characteristics of human intelligence or even better. These fabulous machines possess all our senses and can think like humans. There are technologies which are able to perform specific tasks or may be better than humans. This category falls into narrow AI. Image classification on Pinterest and face recognition in Facebook is an implementation of narrow AI. These technologies exhibit these facets of human intelligence with the help of machine learning.

Machine Learning

Machine learning is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So instead of hand coding, software routines with a set of instructions to accomplish a particular task, the machine is trained using a large amount of data and algorithms that gives the ability to perform tasks. Considering an example is how machine learning is used to make a drastic improvement in machine/computer vision. For example, millions of pictures of humans and also pictures of humans with a cat is tagged. Then the algorithm tries to build a model on its own that can accurately tag a picture of a human and a cat.

Deep Learning

Artificial Neural Network (ANN) is another algorithm approach from the early machine learning family. ANN are inspired by neurons in the brains and the interconnection existing between them. ANN has discrete layers, connections and directions of propaganda. Each layer takes a particular subject to learn. In this layer, depth is created using multiple layers which gives its name deep learning. For example, image recognition in machines is trained via deep learning. Also, in healthcare sector deep learning is useful in identifying indicators of cancer. Google's Alpha Go learned the game and trained against Go Match by tuning its neural network and playing it over and over again.

Relation Between Internet of Things and Artificial Intelligence

Internet of things (IoT) is a system of connected physical objects that are accessible through the internet. As the devices and sensors connected to the Internet of things increases, a large volume of data is being collected. This data collected holds extremely valuable insights. As a blessing in disguise, machine learning and deep learning would require a huge amount of data to work with which can be used from the IoT. From an industrial perspective, AI can be incorporated to machines to predict the need of maintenance or analyze manufacturing processes to make colossal profits in business. From an end user perspective, technology will be tamed and adapted to the customer. Finally, the entwinement of IoT and AI will lead to a phase where machines will point out where the true opportunities are.