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The Buzz Created By This Generative Artificial Intelligence DecodedFew 21st Century innovations have captured the minds of people like ChatGPT has in the last one month. Launched in November 2022, it crossed one million users in just five days.
A now viral tweet puts the magnitude of ChatGPT's success in context – it took Netflix 41 months, Facebook 10 months, and Instagram 2.5 months to gain one million users.
The tool has gained massive traction for its ability to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests, says the OpenAI website.
Noting its ability to churn out human-like conversational responses to questions, futurists have already called ChatGPT a potential tool for education and media.
ChatGPT is just one of the many utilities of the larger technology at play here – Generative AI, which is a part of the larger artificial intelligence family.
"Generative AI focuses on algorithmically creating new data or content, with minimal human involvement, that resembles existing data," explains 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, among other things using the power of machine learning.
Machine Learning (ML), for the unversed, is an application of artificial intelligence to help machines learn better using data and algorithms; for instance: image recognition services.
With its utility in multiple sectors of the economy, Generative AI is expected to boost the data-driven economy. Arun Meena, Founder and CEO of RHA Technologies, says that generative AI will contribute 10 per cent of all generated data by 2025.
According to a recent report by Acumen Research and Consulting, the global generative AI market, which stood at just $7.9 billion in 2021, is projected to grow to $110.8 billion by 2030.
Moreover, the generative AI market will likely grow at a compounded annual growth rate (CAGR) of 34.3 per cent from 2022 to 2030.
Believing that several industries will experience disruption as the AI algorithms mature, Jaideep Kewalramani, head of employability business and COO at TeamLease Edtech, adds: "Generative AI will be able to produce unique pieces of art, literature, write software code, create marketing content, provide fashion tips, develop recipes, have humanised conversations, provide counselling and so much more."
The use cases of Generative AI are still evolving and are expected to reach the human realm, too, for instance – Human Resources Management.
Generative AI can help managers create interview questions for candidates and provide functions like employee onboarding.
Generative AI can also help internal employee communications by automating email responses, translating text, and changing the tone or wording of a text. The technology is expected to make the lives of executives easier by creating presentations based on prompts.
"GAI is likely to make automation more human and communication more transparent across levels. The stronger yet subtle (in terms of being seen) application is in the area of understanding people," says Asif Upadhye, Director at Never Grow Up, a Work Culture Consultancy firm.
Mr Upadhye adds that generative AI may also make hiring better with the use of predictive video or emotional-based tracking.
However, the use of Generative AI by companies leads to a pertinent question – what is the impact of the technology on jobs and employment generation?
There are more complex answers to this question.
Generative AI is a relatively new subset of the larger Artificial Intelligence segment. Hence, reaching a numerical estimate of its impact on the job market is still speculation.
However, the larger AI market will likely lead to the fourth industrial revolution. At least 63 per cent of global CEOs interviewed by PwC in 2019 believed that AI would have a larger impact than the internet.
Another PwC report notes that AI can potentially increase the global GDP by 26 per cent – an estimated $15.7 trillion – by 2030.
A European Union briefing paper quotes McKinsey Global Institute research which suggested that around 70 per cent of companies would adopt at least one type of AI technology by 2030.
However, companies adopting AI are likely to cause disruptions in the workforce, especially in labour-intensive markets like India. According to a World Economic Forum report, AI will likely add 97 million new jobs by 2025 while phasing out 85 million jobs.
The general view is that AI may lead to unemployment in labour-intensive sectors in the short run but will lead to job creation and improved productivity in the long run.
"Countries with larger workforces may face issues in the short term due to loss of jobs having repetitive tasks and an increase in the cost of retraining people who will be replaced by such technologies," opines RHA Technologies' Mr Meena.
He adds that workers with knowledge of AI, ML, and Robotics will be at an advantage in the fourth industrial revolution.
To offset job losses due to AI-driven automation, the focus is increasingly on upskilling and re-skilling employees and job seekers. For instance: Gartner research suggests that 20 per cent of procedural code professionals will have retrained because of disruptions led by generative AI.
"Certain segments of the workforce will come under stress. Industry and academia must come together to re-skill the talent pool and incorporate new skills in the curriculum for undergraduates," says Mr Kewalramani, adding that the fear of generative AI wiping out certain jobs overnight is just a sceptic's viewpoint.
Amid the euphoria around generative AI, it is easy for the common man to lose sight of its pitfalls.
Dr Neog warns that controlling or predicting the outcomes of AI models could be challenging as the algorithms used to have low explainability.
"This can make it difficult to understand how these models make decisions. Additionally, if the data used to train the AI is biased, the generated content may also be biased or flawed," he says.
Given its extensive use (for now) in creating online content, the issues of copyright violation and misinformation could raise their ugly head.
Over time, it will be difficult to understand what constitutes original content, and credits to a piece of derivative work could be questionable, opines Upadhye, adding that the intent of use will matter a lot in the future.
A now viral tweet puts the magnitude of ChatGPT's success in context – it took Netflix 41 months, Facebook 10 months, and Instagram 2.5 months to gain one million users.
ChatGPT is a product of OpenAI, an artificial intelligence research and deployment company launched by Sam Altman and Elon Musk in 2015.
The tool has gained massive traction for its ability to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests, says the OpenAI website.
Noting its ability to churn out human-like conversational responses to questions, futurists have already called ChatGPT a potential tool for education and media.
ChatGPT is just one of the many utilities of the larger technology at play here – Generative AI, which is a part of the larger artificial intelligence family.
"Generative AI focuses on algorithmically creating new data or content, with minimal human involvement, that resembles existing data," explains 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, among other things using the power of machine learning.
Machine Learning (ML), for the unversed, is an application of artificial intelligence to help machines learn better using data and algorithms; for instance: image recognition services.
With its utility in multiple sectors of the economy, Generative AI is expected to boost the data-driven economy. Arun Meena, Founder and CEO of RHA Technologies, says that generative AI will contribute 10 per cent of all generated data by 2025.
According to a recent report by Acumen Research and Consulting, the global generative AI market, which stood at just $7.9 billion in 2021, is projected to grow to $110.8 billion by 2030.
Moreover, the generative AI market will likely grow at a compounded annual growth rate (CAGR) of 34.3 per cent from 2022 to 2030.
Believing that several industries will experience disruption as the AI algorithms mature, Jaideep Kewalramani, head of employability business and COO at TeamLease Edtech, adds: "Generative AI will be able to produce unique pieces of art, literature, write software code, create marketing content, provide fashion tips, develop recipes, have humanised conversations, provide counselling and so much more."
The use cases of Generative AI are still evolving and are expected to reach the human realm, too, for instance – Human Resources Management.
Generative AI can help managers create interview questions for candidates and provide functions like employee onboarding.
Generative AI can also help internal employee communications by automating email responses, translating text, and changing the tone or wording of a text. The technology is expected to make the lives of executives easier by creating presentations based on prompts.
"GAI is likely to make automation more human and communication more transparent across levels. The stronger yet subtle (in terms of being seen) application is in the area of understanding people," says Asif Upadhye, Director at Never Grow Up, a Work Culture Consultancy firm.
Mr Upadhye adds that generative AI may also make hiring better with the use of predictive video or emotional-based tracking.
However, the use of Generative AI by companies leads to a pertinent question – what is the impact of the technology on jobs and employment generation?
There are more complex answers to this question.
Generative AI is a relatively new subset of the larger Artificial Intelligence segment. Hence, reaching a numerical estimate of its impact on the job market is still speculation.
However, the larger AI market will likely lead to the fourth industrial revolution. At least 63 per cent of global CEOs interviewed by PwC in 2019 believed that AI would have a larger impact than the internet.
Another PwC report notes that AI can potentially increase the global GDP by 26 per cent – an estimated $15.7 trillion – by 2030.
A European Union briefing paper quotes McKinsey Global Institute research which suggested that around 70 per cent of companies would adopt at least one type of AI technology by 2030.
However, companies adopting AI are likely to cause disruptions in the workforce, especially in labour-intensive markets like India. According to a World Economic Forum report, AI will likely add 97 million new jobs by 2025 while phasing out 85 million jobs.
The general view is that AI may lead to unemployment in labour-intensive sectors in the short run but will lead to job creation and improved productivity in the long run.
"Countries with larger workforces may face issues in the short term due to loss of jobs having repetitive tasks and an increase in the cost of retraining people who will be replaced by such technologies," opines RHA Technologies' Mr Meena.
He adds that workers with knowledge of AI, ML, and Robotics will be at an advantage in the fourth industrial revolution.
To offset job losses due to AI-driven automation, the focus is increasingly on upskilling and re-skilling employees and job seekers. For instance: Gartner research suggests that 20 per cent of procedural code professionals will have retrained because of disruptions led by generative AI.
"Certain segments of the workforce will come under stress. Industry and academia must come together to re-skill the talent pool and incorporate new skills in the curriculum for undergraduates," says Mr Kewalramani, adding that the fear of generative AI wiping out certain jobs overnight is just a sceptic's viewpoint.
Amid the euphoria around generative AI, it is easy for the common man to lose sight of its pitfalls.
Dr Neog warns that controlling or predicting the outcomes of AI models could be challenging as the algorithms used to have low explainability.
"This can make it difficult to understand how these models make decisions. Additionally, if the data used to train the AI is biased, the generated content may also be biased or flawed," he says.
Given its extensive use (for now) in creating online content, the issues of copyright violation and misinformation could raise their ugly head.
Over time, it will be difficult to understand what constitutes original content, and credits to a piece of derivative work could be questionable, opines Upadhye, adding that the intent of use will matter a lot in the future.