A Conundrum of Artificial Intelligence, Machine Learning and Data Science

Technologies like AI, ML and DS are some of the popular buzzing words of the era and every next person is talking about them. But, the easier it seems the more complex they are to solve. Mostly, people get confused by these words so, here in this article I’m covering almost every detail (as possible) about them.  

Almost every organisation is adopting them at a rapid rate and will continue its surge in the next year as well. Despite that, we haven’t decided how we will manage the technology if we will not be able to break it down?

Maybe this issue will come in the next year. But for now we are in the mirage of advancements of the technology and we are not even trying to give a second thought what will happen if we are unable to outsource the critical thought. What’s the need to get hurt if Watson, Alexa, Google Assistant can do that thing?

Before that, understand what is AI, ML and DS?

AI is an imitation of the human brain which makes the machine to function. ANN(Artificial Neural Network) is one of the advanced kind which makes the machine work like a human brain means the exact replica of a human. This is an excellent tool which finds the solution of the complex problem and recognises. The primary function of AI includes learning, logical reasoning and self-correction. Despite that, is one of the complex technology to work. The machine does not inherit automatically so, to make them stimulate like human brain we need a lot of data and computing power.

Artificial Intelligence includes Machine Learning which makes the system to learn from the algorithm and improve itself. It mainly focuses on collecting the data to gain insight and make predictions based on the past data. To achieve excellence in understanding the concepts of AI implemented ML one can enrol in the Machine Learning course which will lift your professional as well as personal growth in a short interval of time. Therefore, ML turns out to be one of the lucrative jobs of the era. ML are divided into three type of model i.e supervised, unsupervised and reinforcement learning. Supervised learning, the levelled data is used to recognise the characteristics of the machine for the future purpose while in Unsupervised, the unlabeled data makes the machine understand and classify the data. Reinforcement learning, the machine interacts with the surrounding through algorithms to analyse the error.      

Data Science is gaining the insight of data by analysing the data. To extract the data various type of techniques are implemented such as machine learning, mathematics, statistical modelling, data engineering, visualisation, uncertainty modelling, cloud computing and data warehousing. Most of the time, big data is not involved in data science but the fact is scaling up of data makes big data an important feature.

Though data science is a lot more than machine learning. It is not necessary that the data in data science comes from a machine or through a mechanical process. The major difference between Data Science and Machine Learning is that it covers every aspect of data processing, it is not just about statistics and algorithms. Data visualisation, Distributed Architecture, Data Visualisation, Deployment, Data engineering, Data integration and much more comes under data science.

Reality Check:

In a study, it is estimated that the AI technology will touch the figure of $19.1billion by the end of this year and by 2021, $52.2 billion. But, implementations of AI will be successful, is not necessary. Why? It is because the challenges faced while managing the integration will be the difficult task. And, at that time, it will be similar to as ERP, HCM, etc and hence, not more pleasing.

There’s a lot with AI, ML and data science but still, the implementation is in-digestive because of the sprawl of algorithms. Sooner, we will realise the theory of Gartner was right. According to him, AI will cover the whole world, the technologies like artificial general intelligence, deep neural networks, platforms such as PaaS will be at mainstream in a couple of years. Important is how marketing management figures out the application and use of AI.

About The Author

Danish Wadhwa is a doyen of governing the digital content to assemble good relationships for enterprises or individuals. He is specialized in digital marketing, cloud computing, web designing and offers other valuable IT services for organizations, eventually enhancing their shape by delivering the stupendous solutions to their business problems.

One Reply to “A Conundrum of Artificial Intelligence, Machine Learning and Data Science”

  1. keep posting articles like this.. its a good one for beginners to understand what exactly the stuff is ..! 🙂

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