Artificial Intelligence And Machine Learning: Still A Confusion!

Today, the change in the business landscape is the effect of the most disruptive and buzzed technologies namely, Machine Learning (ML) and Artificial Intelligence (AI).

However, these tags don’t sound strange to us, but the introduction of the entire fuss is still anonymous.

With the advanced algorithm, the enterprises are found busy in controlling the computational power and digital data explosion to allow the natural and collaborative interactions between the machines and people.

ML and AI are still a mystery among the media as well as the public.

Most of the beings prefer writing AL and ML technologies in spite of ML and AI and the arguments strike in between which states that the former corresponds with an individual mind. Sometimes used as synonyms and sometimes as discrete, in reality, ML to AI is what neurons are to the human mind. Firstly, now we’ll start with ML.

Branch of AI is ML, as stated by Carnegie Mellon University’s Editor of Machine Learning Department, Roberto Iriondo in Pennsylvania.

In the words of Machine Learning pioneer and computer scientists, Tom M. Mitchell, “ML is the study of computer algorithms that allow computer programmes to automatically improve through experience.”

Iriondo added more to let us understand easily that, “In a simple example, if you load an ML programme with a considerable large data-set of X-ray pictures along their description (symptoms, etc), it will have the capacity to assist (or perhaps automatize) the data analysis of X-ray pictures later on.”

In the data-set, each picture will cross the sight of ML model to let it fetch the common patterns in the pictures that are designated with the analogous indications.

Artificial Intelligence and Machine Learning

On the contrary, the scope of AI is exceptionally wide and is not only an autonomous data model but also a system alone. Simply, we can say that AI is meant for the creation of computers that reveals a behaviour akin to humans.

Microsoft’s Customer Success Unit, Cloud Solution Architect, Thro Van Krray, said that it would be a useless attempt to define AI because the first effort should be in towards defining “intelligence” properly, a word that craves to connotations’ variety.

He too appended that, “Firstly, it is interesting and important to note that the technical difference between what used to be referred to as AI over 20 years ago and traditional computer systems, is close to zero.”

Today, AI systems are reflecting human being’s vital characteristic that keeps us detached from the traditional computer systems, the prediction machines point human beings.

Today, like humans, AI systems are mostly complex prediction machines.

Kraay also told that, “The more sophisticated the machine, the more it is able to make accurate predictions based on a complex array of data used to train various (ML) models, and the most sophisticated AI systems of all are able to continually learn from faulty assertions in order to improve the accuracy of their predictions, thus exhibiting something approximating human intelligence.”

On the static data-sets, most of the ML algorithms are taught to give birth to predictive models, so that in the AI definition, a section of dynamic can only be facilitated by ML algorithms.

About fifty years before, an AI form was considered as a programme of chess-playing. As it’s available on almost every computer, that’s why a chess game is deemed as antiquated and dull.

Iriondo again said that “AI today is symbolized with human-AI interaction gadgets like Google Home, Apple Siri, and Amazon Alexa or ML-powered video prediction systems that power Netflix, Amazon, and YouTube.”

Opposite to ML, a running target, AI and its definition alter as its relevant technological advancements lead to developing further.

Again Iriondo quips that “Possibly, within a few decades, today’s innovative AI advancements will be considered as dull as flip-phones are to us right now.”