Is AI Breaking Up with Machine Learning?

Posted by Steve rose on December 22nd, 2019

In the booming Artificial Intelligence (AI) economy, we are slowly sliding into an era of excitement. We suddenly have a diverse range of AI and automation projects, 3 million new job titles and vacancies, and more than 6 million new data sources that were inexistent in 2016. That’s the power of AI we are witnessing. But, wait! We are not to be confused with new emerging technologies and existing ones that have fractured the ambitions and forecasts in the job market. One of those fractured associations or pairing is that of AI and Machine Learning (AI and ML).

These technologies are paired so closely together (misleading!!!) that it becomes hard for applicants in the Business Analyst Course to truly understand which one to learn first, and if they are different!

AI and ML are Different. That’s Period.

While the world is crazy about AI, there is some abrasion regarding the role of Machine Learning and its potential applications. For an easy understanding, AI is a straight forward science. Machine Learning, on the other hand, is much deeper and based on higher computing logic built using coding semantics, augmented science, and supervised deep learning techniques.

Bad Analytics: AI is Mostly Narrow; ML is Vague

Business Analytics can go off track if the AI and ML re both wayward in their objectives. All these boil down to the way data is mined, analyzed, and modeled for algorithms. Most BI tools are built on the standard (ambiguous) practice of placing AI as an all-encompassing technique of narrowly segmenting every question asked. On the other hand, ML is still vague as far as keeping its objectives clear.

For example, how would an ML describe a ‘Jackfruit’?

ML algos could provide multiple answers, based on what inputs are objectified.

Possible answers are:

“Jackfruit is fruit.”

“It’s rough.”

“It’s ripe”

“It’s green.”

AI tool would be able to answer something more. For example,

Yes, it’s a jackfruit, ripe and green.

In short, it can be jointly stated that AI could be using a combination of ML algorithms.

It’s not a jackfruit!

Experts Say, “Machine Learning is the Bone, AI is Muscle.”

AI is the application part of the machine level algorithms. AI models can only be applied once data analysts are ready with the whole data mechanism and models to deploy machine learning algorithms.

Is it hard to understand? Yes, it is… that is why we have more  data analysts and business intelligence engineers compared to data scientists. AI is a holistic scientific concept that thrived much later than the eruption of Deep Learning and Machine Learning, promising to take AI ML diversions to a new level.

Overall, it can be stated that AI is breaking away from generic machine learning practices.