Is AI Just Automation + Data?

IN RECENT MONTHS, the term “AI” has become a buzzword in technology and business circles, often used to describe a wide range of innovations

- Srinath Sridharan

For those of us from non-tech industries, ChatGPT’s introduction a few months ago was the biggest trigger for our AIspeak.

One of the common myths that AI is simply just automation + data, probably trivialises a field that has rich history, a rapidly evolving present, and a future with impossible possibilities and plausibilities. The concept of artificial intelligence has been around for over half a century. It originated in the 1950s when pioneers like Alan Turing and John McCarthy began exploring the idea of creating machines that could simulate human intelligence.

Early AI systems were rule-based and had limited capabilities, but they sparked a fascination with the potential of machines to mimic human cognition. Over the ensuing decades, AI underwent significant advancements. Expert systems, neural networks, and machine learning algorithms emerged, each contributing to the field’s growth. Artificial Intelligence started to excel in areas like game playing, language processing, and data analysis. However, its widespread adoption remained limited due to computational constraints and the absence of vast datasets.

The turning point came in recent years with the explosion of data and computing power. The availability of vast amounts of data, combined with more power ful processors and improved algorithms, allowed AI systems to surpass human capabilities in tasks such as image recognition and natural language understanding. This data-centric approach has indeed been a crucial component of AI’s recent success.

”Is AI just Automation + Data? That’s like saying humans are just bones + flesh. Sure, it’s an essential part, but there’s a whole lot of magic happening in the brain circuits!”