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My thoughts on Defining A.I.

I keep going back to an article documenting Michael I. Jordan‘s thoughts on the narrative behind A.I. systems. Jordan leads with the sweeping statement “Stop calling everything A.I.”. He’s  right. There appears to be a prevalent trend within this industry where people are mixing terminology under the term A.I. I have in some cases been guilty of describing Robotic Process Automation under the category of A.I. to customers to keep things simple and centralised under that category.

The more I think about this, the more I feel that we have a responsibility to educate the market as much as possible and think about decentralising the term A.I. in our communications. I would also go further and outline that saying that anything is possible with A.I. in my opinion is simply untrue for the decade we live in right now. A.I. is a tool, that’s all it is right now. A.I. systems are not in a position to replace humans in a huge number of tasks or jobs that require social interaction, rational thinking and creative thinking. We have a responsibility to communicate that to people also.

In my opinion, we need to change the conversation around A.I. I think we need to be transparent about the capabilities of A.I. and instead of telling people what the benefits of its use are; we should help them understand how it works so that they can get there themselves. From an individual perspective, the lack of explanation for many basic terms may be a contributory factor to the ominous narrative around A.I.

The purpose of this post is to pick apart what A.I. is and shed light on the components that helps it do its job for whatever purpose it has been built for. There is another post that defines the term “A.I.” in more detail and it can be found here.

Breaking the term A.I. down

A.I. in many cases is the creation of algorithms to categorise, analyse and draw forecasts from data it has been fed. However it can be confused with Robotic Process Automation and other subsets such as Machine Learning, Computer Vision and Natural Language Processing. Terms like these are are used interchangeably within the realm of A.I, this can lead to confusion. This section will breakdown the comparisons of each and explain clearly what they are for and what role they play in the world of A.I. and Software Development. There is a lot of terms within the A.I. space that are used interchangeably, this can lead to confusion in many cases.

The difference between machine learning and A.I.

Machine Learning is a subset of A.I. that is specifically centred around the use of algorithms to predict new trends. A.I. then analyses this data, and determines what to do with it based on how it has been programmed. The A.I. model executes the tasks smartly based on the accuracy of the data it has been fed by the machine learning model. The goal of machine learning is to teach computers to learn for themselves. The A.I. model/software decides what to do with that knowledge.

The difference between Robotic Process Automation and A.I.

Robotic Process Automation (RPA) is the mimicry of a human’s action(s) on a computer. They are the clicking on certain areas, copying and pasting or the movement of a cursor on a screen. This can also be applied at a much more industrial scale in terms of the movement of a machine. A really easy way of looking at it would be viewing A.I. as the brain, and RPA as the body. A.I. is the thinking behind a certain task and RPA is the process or the execution of the task.

The difference between the term Natural Language Processing and A.I.

Natural Language Processing (NLP) is a subset or a branch of A.I. that can interpret a language. NLP enables the A.I. platform/software to understand and manipulate the language for whatever purpose it has been built to determine. Smart assistants such as Alexa, Siri and Cortana would be examples of NLP technology.

The difference between the term Computer Vision and A.I.

Computer Vision is a subset of A.I. that enables a machine to visually recognise what it sees. With the help of Machine Learning, Computer Vision can be a very robust solution to help businesses speed up their visual inspection processes or spot defects in a production line.

Have you seen a pattern yet? A.I. is an umbrella term in many cases. There is nothing wrong with the use of umbrella terms but using it to describe specific areas of technology, can add to confusion. As a result of this, people may not know to any large degree how their problem can be solved. Thought leaders, developers and businesses delivering services in this area have the capability to educate the market rather than simplifying their marketing strategy by using an umbrella term to describe everything.

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