Introducing Artificial Intelligence and Machine Learning
The booming rise of our technological advancement gave prominence to Artificial Intelligence (AI) and Machine Learning (ML) concepts. While we ponder upon the wonders and possibilities of future technology, workspace, business and society as a whole, it cannot be denied that the concepts above are integral to the discussions amongst business and technology executives, as they’re seeking to understand more about the possibility of their implications, advantages and hazards.
Given the buzz surrounding AI and ML, their core technology can be traced back to more than a century ago when linear regression models were present in the nineteenth century, while neural networks were formulated in the twentieth. Consequently, in the twenty-first century, the application of complex algorithms on concrete business issues became more prevalent, due to the ubiquitous access towards massive quantities of information and an increase in faster processors and processing capacity.
In an article from Exploding Topics, statistics show that user-generated data increased at an exponential rate. The exponential jump of 4.4 zettabytes (ZB) of data in the digital universe during the year 2019, grew tenfold, to a whopping 44 ZB in the following year. Today, approximately 2.5 quintillion bytes of data are generated every day, with over 70 per cent of the world’s data being created by humans.
However, the terms AI and ML are used as they are related but carry different meanings. The general public and media may be uncertain about what AI and ML are and the differences between them. In some other circumstances, these terms are employed as distinct, parallel improvements, while others may leverage the trend to generate anticipation and hype to enhance sales and earnings.
A research article by The Verge in 2019 addressed the misappropriation of organisations alleging to apply AI in their services and goods, as far as up to 40% of European startups, whereby the claim can potentially obtain anywhere from 15-50% more funding compared to other startups. In a 2018 TechTalks article as part of their Demystifying AI series, it is even found that some companies will go about claiming to use sophisticated AI and ML to gather and analyse touch points in mobile apps to improve user experience and forecasting user behaviour.