Opinion

Is the golden age of information science finally over?

If you happen to are reading this text, you almost certainly have already got a job in the information industry, or want to get into the sphere.

And with all of the advancements which have been made in the sphere of generative AI previously 12 months, you’re concerned about whether data science jobs will likely be automated away.

One 12 months ago, I might’ve scoffed at anyone who even brought up the opportunity of automating my data science job.

Actually, I even wrote a complete article mocking the concept that AI could ever replace data scientists—I mean, we write code, construct machine learning models, analyze data, and break down complex information to non-technical stakeholders.

Our job is difficult. These skills take years to hone. AI could improve efficiency and collaboration between data teams, nevertheless it couldn’t possibly replace the actual work we were doing.

The above blog post, nonetheless, was written before ChatGPT was released.

Since then, we’ve got witnessed paradigm-shifting advancements in the sphere of generative AI.

In this text, I’ll re-evaluate my stance on the longer term of information science based on existing developments in the sphere of generative AI.

Based on my extensive research and insights from industry experts, I’ll present a spread of viewpoints explaining why ChatGPT might replace data scientists, in addition to the the explanation why it might not.

I’ll examine either side of the controversy and leave it to you, the reader, to make an informed decision as as to if generative AI will render data scientists obsolete.

1. ChatGPT can write code…fast

Data scientists spend around 40%-50% of their time writing code.

And never only can ChatGPT write code, it also became really good at it, really fast.

The chatbot has passed coding interviews at multiple top corporations, can turn hand-drawn sketches into fully-fledged web sites, and designers…