AI/ML

Artificial Intelligence (AI) and Machine Learning (ML) are the two most hot topics right now, and often seen to be used interchangeably. Both terms crop up very frequently when the topic is Big Data,analytics. In short, Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart". And, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

Artificial Intelligence devices can be classified into two fundamental groups-applied or general. Applied AI is -systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle. Generalized Ais- systems or devices which can in theory handle any task- are less common. It is the area that has led to the development of Machine Learning. 

Two important breakthroughs led to the emergence of Machine Learning. One of this was when Arthur Samuel in 1959 deduced that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves. The second, was the emergence of the internet, and the huge increase in the amount of data being generated, stored, and made available for analysis. With this  engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the advantages they hold over us such as speed, accuracy and lack of bias. A Neural Network is a computer system designed to work by classifying information in the same way a human brain do. It can be taught to recognize, for example, images, and classify them according to elememts they contain. Essentially it works on probability-based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty 

Another field of AI which is well known is Natural Language Processing (NLP). NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likeley to comprehend.

The fact that we will eventually develop human-like AI is today possible and certainly today we are even more closer towards this goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which has been brought about by ML.