Introduction

       

Introduction To AI / ML !!

What is Artificial Intelligence (AI)

AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognizing objects, recognizing and making sense of speech, and decision making in a constrained environment.

Narrow AI:

                  The field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalize to unseen domains. This is the form of AI that we have today, for example Google Translate.

Artificial General Intelligence (AGI):

                                                             A form of AI that can accomplish any intellectual task that a human being can do. It is more conscious and makes decisions similar to the way humans take decisions. AGI remains an aspiration at this moment in time with various forecasts ranging from 2029 to 2049 or even never in terms of its arrival. It may arrive within the next 20 or so years but it has challenges relating to hardware, energy consumption required in today’s powerful machines, and the need to solve for catastrophic memory loss that affects even the most advanced deep learning algorithms of today.

Super Intelligence: 

                                Is a form of intelligence that exceeds the performance of humans in all domains. This refers to aspects like general wisdom, problem solving and creativity.

Classical Artificial Intelligence: 

                                                     Are algorithms and approaches including rules-based systems, search algorithms that entailed uninformed search (breadth first, depth first, universal cost search), and informed search such as A and A* algorithms. These laid a strong foundation for more advanced approaches today that are better suited to large search spaces and big data sets. It also entailed approaches from logic, involving propositional and predicate calculus. Whilst such approaches are suitable for deterministic scenarios, the problems encountered in the real world are often better suited to probabilistic approaches.

The field has been making major impact in recent times across various sectors including Health Care, Financial Services, Retail, Marketing, Transport, Security, Manufacturing and Travel sectors.

The advent of Big Data, driven by the arrival of the internet, smart mobile and social media has enabled AI algorithms, in particular from Machine Learning and Deep Learning, to leverage Big Data and perform their tasks more optimally. This combined with cheaper and more powerful hardware such as Graphical Processing Units (GPUs) has enabled AI to evolve into more complex architectures.

Machine Learning 

Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959.