Couple 21st Century innovations have captured the minds of the likes of ChatGPT over the past month. It was launched in November 2022 and reached one million users in just five days.
A now-viral tweet puts the magnitude of ChatGPT’s success into context: It took Netflix 41 months, Facebook 10 months, and Instagram 2.5 months to reach a million users.
ChatGPT reached over 1 million users in just 5 days.
For comparison: Netflix took 41 months, FB – 10 months and Instagram – 2.5 months.
But many have not yet realized its full potential.
Here are the 10 amazing things you can do with it right now:
— Aleksandr Volodarsky 🇺🇦 (@volodarik) December 8, 2022
ChatGPT is a product of OpenAI, an artificial intelligence research and implementation company founded in 2015 by Sam Altman and Elon Musk.
The tool has gained tremendous traction for its ability to answer follow-up questions, admit mistakes, challenge erroneous assumptions and reject inappropriate requests, the OpenAI website says.
Given its ability to produce human-like conversational responses to questions, futurists have already called ChatGPT a potential tool for education and media.
ChatGPT is just one of many utilities of the larger technology at play here: generative AI, which is part of the larger artificial intelligence family.
“Generative AI focuses on algorithmically creating new data or content, with minimal human intervention, that resembles existing data,” explained Dr. Debanga Raj Neog, assistant professor at Mehta Family School of Data Science and Artificial Intelligence at IIT Guwahati.
Dr. Neog adds that generative AI can produce images, videos, text, music, 3D models and websites, using machine learning, among other things.
Machine Learning (ML), for the inexperienced, is an application of artificial intelligence to help machines learn better using data and algorithms; for example: image recognition services.
With its utility across multiple sectors of the economy, generative AI is expected to boost the data-driven economy. Arun Meena, founder and CEO of RHA Technologies, says generative AI will contribute 10 percent of all data generated by 2025.
According to a recent report from Acumen Research and Consulting, the global generative AI market, which was just $7.9 billion in 2021, is expected to grow to $110.8 billion by 2030.
Moreover, between 2022 and 2030, the generative AI market is likely to grow at a compound annual growth rate (CAGR) of 34.3 percent.
Jaideep Kewalramani, Head of Employability Business and COO at TeamLease Edtech, believes several industries will experience disruption as AI algorithms mature: “Generative AI will be able to produce unique artworks and literature, write software code and create marketing content , give fashion tips, develop recipes, have human conversations, give advice and much more.”
The use cases of generative AI are still evolving and are expected to reach the human domain as well, e.g. Human Resources Management.
Generative AI can help managers craft interview questions for candidates and provide features like employee onboarding.
Generative AI can also help employees’ internal communications by automating email responses, translating text, and changing the tone or wording of a text. The technology is expected to make life easier for executives by creating presentations based on prompts.
“GAI is likely to make automation more human and communication more transparent across levels. Its stronger but subtle (in terms of being seen) application is in the realm of understanding people,” said Asif Upadhye, director of Never Grow Up, a Work Culture Consultancy.
Mr Upadhye adds that generative AI can also make hiring better through the use of predictive video or emotion-based tracking.
However, the use of generative AI by companies leads to a pertinent question: what is the technology’s impact on jobs and employment generation?
There are more complex answers to this question.
Generative AI is a relatively new subset of the larger segment of artificial intelligence. So it is still speculation to make a numerical estimate of the impact on the labor market.
However, the larger AI market is likely to lead to the fourth industrial revolution. At least 63 percent of global CEOs interviewed by PwC in 2019 believed AI would have a bigger impact than the internet.
Another PwC report notes that AI could potentially increase global GDP by 26 percent by 2030 — an estimated $15.7 trillion.
A briefing paper from the European Union cites research from McKinsey Global Institute that suggested about 70 percent of companies would adopt at least one type of AI technology by 2030.
However, companies adopting AI are likely to cause workforce disruptions, especially in labor intensive markets such as India. According to a report by the World Economic Forum, AI is likely to create 97 million new jobs by 2025 while phasing out 85 million jobs at the same time.
The general view is that AI can lead to unemployment in labour-intensive sectors in the short term, but to job creation and productivity improvement in the long term.
“Countries with larger workforces may face challenges in the short term due to the loss of jobs with repetitive tasks and increased costs of retraining people who will be replaced by such technologies,” said Meena, Mr. Meena of RHA Technologies.
He adds that workers with knowledge of AI, ML and Robotics will have an advantage in the fourth industrial revolution.
To compensate for job losses due to AI-driven automation, the focus is increasingly on upskilling and reskilling employees and job seekers. For example: Gartner research suggests that 20 percent of procedural code professionals will have retrained due to disruptions caused by generative AI.
“Certain segments of the workforce will come under pressure. Industry and academia must work together to upskill the talent pool and incorporate new skills into the curriculum for students,” said Mr. Kewalramani, adding that the fear that generative AI wipes out certain jobs overnight is just a skeptic’s point of view.
Amidst the euphoria surrounding generative AI, it’s easy for the common man to lose sight of the pitfalls.
Dr. Neog warns that controlling or predicting the outcomes of AI models can be challenging, as the algorithms used to have little explainability.
“This can make it difficult to understand how these models make decisions. In addition, if the data used to train the AI is biased, the content generated may also be biased or flawed,” he says.
Given its extensive use (for now) in online content creation, the issues of copyright infringement and misinformation could arise.
Over time, it will be difficult to understand what original content is, and credits for a derivative work can be questionable, Upadhye believes, adding that the intent of use will matter a lot in the future.
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