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Nilekani warns of technological dead ends: ‘Businesses must future-proof AI’

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AI architecture must facilitate an approach that combines the analytical thinking of the left brain with the intuitive approach of the right brain

New Delhi: To keep up with fast-paced technology, businesses need to “future-proof” their AI infrastructure to avoid getting stuck at a dead-end with outdated technology, Infosys Chairman Nandan Nilekani said.

With nations around the world regulating artificial intelligence (AI), he wrote in Infosys’s annual report that companies need to develop their applications to ensure compliance with different regulations.

“Given that the leaderboard of technologies will be changing at a bewildering pace, enterprises will have to ‘future proof’ their AI infrastructure with suitable abstractions to be able to switch models easily and not be trapped in a technological cul de sac,” Nilekani said.

He further said the enterprises would need an AI foundry for experimentation and an AI factory for scaling up.

“AI architecture must facilitate an approach that combines the analytical thinking of the left brain with the intuitive approach of the right brain. The constraint of resources will require a transparent way of identifying the highest value AI use cases,” he said.

Nilekani, 68, said the brooding period of the GenAI revolution is over as clarity has emerged from the chaos and chatter of the last 18 months.

“The initial hyperventilation of AI doomers and the risk of human extinction by AI advances like Artificial General Intelligence (AGI) has quietened down… gen AI has enormous potential for good when explored and advanced within the guardrails of responsibility.

“Many of the doomsday prophets pleading for extensive AI regulation have revealed themselves to be just protectionists who want to limit the fruits of gen AI to a few companies and investors,” he said.

There won’t be a scenario where there will be ‘one model to rule them all’, Nilekani said.

He said the real power of AI will come from configuring all the different models and tools to get the best solutions.

“This is not very different from previous generations of technology. What’s more, the rise of powerful open-source AI models has accelerated the deployment of AI to solve tough business and societal challenges.

“Although there could be concentration risk in the hardware and cloud infrastructure space, as we move into actual use cases, a thousand flowers will bloom,” he said.

He said gains from automation must lead to talent redeployment in new areas with new opportunities.

“We must learn from applying AI to ourselves, be it in creating an AI-first enterprise or in accelerating the massive talent amplification that’s now needed,” he said.

Change will have to be embraced — not resisted, he added.

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